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+
+From phd@EMBL-Heidelberg.de Wed Nov 25 10:24:25 1998
+Date: Tue, 24 Nov 1998 17:45:25 +0100
+From: Protein Prediction <phd@EMBL-Heidelberg.de>
+To: eric.beitz@uni-tuebingen.de
+Subject: PredictProtein
+
+The following information has been received by the server:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+________________________________________________________________________________
+
+reference predict_h25873 (Tue Nov 24 17:43:21 MET 1998)
+from eric.beitz@uni-tuebingen.de
+password(###)
+resp MAIL
+orig HTML
+prediction of: -secondary structure (PHDsec)-solvent accessibility (PHDacc)-
+return msf format
+# no description
+MASEIKKKLFWRAVVAEFLAMTLFVFISIGSALGFNYPLERNQTLVQDNVKVSLAFGLSIATLAQSVGHISGAHSNPAVT
+LGLLLSCQISILRAVMYIIAQCVGAIVASAILSGITSSLLENSLGRNDLARGVNSGQGLGIEIIGTLQLVLCVLATTDRR
+RRDLGGSAPLAIGLSVALGHLLAIDYTGCGINPARSFGSAVLTRNFSNHWIFWVGPFIGSALAVLIYDFILAPRSSDFTD
+RMKVWTSGQVEEYDLDADDINSRVEMKPK
+
+________________________________________________________________________________
+
+Result of PROSITE search (Amos Bairoch):
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+please quote: A Bairoch, P Bucher & K Hofmann: The PROSITE database,
+its status in 1997. Nucl. Acids Res., 1997, 25, 217-221.
+
+________________________________________________________________________________
+
+--------------------------------------------------------
+
+--------------------------------------------------------
+
+Pattern-ID: ASN_GLYCOSYLATION PS00001 PDOC00001
+Pattern-DE: N-glycosylation site
+Pattern: N[^P][ST][^P]
+ 42 NQTL
+ 250 NFSN
+
+Pattern-ID: GLYCOSAMINOGLYCAN PS00002 PDOC00002
+Pattern-DE: Glycosaminoglycan attachment site
+Pattern: SG.G
+ 135 SGQG
+
+Pattern-ID: PKC_PHOSPHO_SITE PS00005 PDOC00005
+Pattern-DE: Protein kinase C phosphorylation site
+Pattern: [ST].[RK]
+ 157 TDR
+ 398 TDR
+
+Pattern-ID: CK2_PHOSPHO_SITE PS00006 PDOC00006
+Pattern-DE: Casein kinase II phosphorylation site
+Pattern: [ST].{2}[DE]
+ 118 SLLE
+ 383 SRVE
+
+Pattern-ID: MYRISTYL PS00008 PDOC00008
+Pattern-DE: N-myristoylation site
+Pattern: G[^EDRKHPFYW].{2}[STAGCN][^P]
+ 30 GSALGF
+ 92 GLSIAT
+ 179 GLLLSC
+ 288 GAIVAS
+ 407 GITSSL
+ 544 GVNSGQ
+ 722 GLSVAL
+ 917 GINPAR
+ 1141 GSALAV
+
+Pattern-ID: PROKAR_LIPOPROTEIN PS00013 PDOC00013
+Pattern-DE: Prokaryotic membrane lipoprotein lipid attachment site
+Pattern: [^DERK]{6}[LIVMFWSTAG]{2}[LIVMFYSTAGCQ][AGS]C
+ 77 PAVTLGLLLSC
+
+Pattern-ID: MIP PS00221 PDOC00193
+Pattern-DE: MIP family signature
+Pattern: [HNQA].NP[STA][LIVMF][ST][LIVMF][GSTAFY]
+ 74 HSNPAVTLG
+
+________________________________________________________________________________
+
+Result of ProDom domain search (Corpet, Gouzy, Kahn):
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+- please quote: ELL Sonnhammer & D Kahn, Prot. Sci., 1994, 3, 482-492
+
+________________________________________________________________________________
+
+--- ------------------------------------------------------------
+--- Results from running BLAST against PRODOM domains
+---
+--- PLEASE quote:
+--- F Corpet, J Gouzy, D Kahn (1998). The ProDom database
+--- of protein domain families. Nucleic Ac Res 26:323-326.
+---
+--- BEGIN of BLASTP output
+BLASTP 1.4.7 [16-Oct-94] [Build 17:06:52 Oct 31 1994]
+
+Reference: Altschul, Stephen F., Warren Gish, Webb Miller, Eugene W. Myers,
+and David J. Lipman (1990). Basic local alignment search tool. J. Mol. Biol.
+215:403-10.
+
+Query= prot (#) ppOld, no description /home/phd/server/work/predict_h25873
+ (269 letters)
+
+Database: /home/phd/ut/prodom/prodom_34_2
+ 53,597 sequences; 6,740,067 total letters.
+Searching..................................................done
+
+ Smallest
+ Sum
+ High Probability
+Sequences producing High-scoring Segment Pairs: Score P(N) N
+
+ 390 p34.2 (45) MIP(6) AQP1(4) GLPF(4) // PROTEIN INTRIN... 270 2.0e-32 1
+ 45663 p34.2 (1) AQPZ_ECOLI // AQUAPORIN Z. 90 3.2e-13 2
+ 45611 p34.2 (1) AQP2_HUMAN // AQUAPORIN-CD (AQP-CD) (WAT... 136 6.0e-13 1
+ 304 p34.2 (61) AQP2(10) GLPF(6) MIP(5) // PROTEIN CHANN... 121 9.2e-11 1
+ 45607 p34.2 (1) PMIP_NICAL // POLLEN-SPECIFIC MEMBRANE I... 80 1.2e-07 2
+ 45606 p34.2 (1) BIB_DROME // NEUROGENIC PROTEIN BIG BRAIN. 80 1.2e-05 2
+ 2027 p34.2 (15) GLPF(9) AQP3(2) // PROTEIN FACILITATOR ... 60 3.4e-05 2
+ 45615 p34.2 (1) GLPF_STRPN // GLYCEROL UPTAKE FACILITATO... 63 0.024 1
+ 45638 p34.2 (1) AQP5_HUMAN // AQUAPORIN 5. 61 0.044 1
+
+>390 p34.2 (45) MIP(6) AQP1(4) GLPF(4) // PROTEIN INTRINSIC CHANNEL WATER
+ AQUAPORIN TONOPLAST MEMBRANE FOR PLASMA LENS
+ Length = 88
+
+ Score = 270 (125.3 bits), Expect = 2.0e-32, P = 2.0e-32
+ Identities = 47/67 (70%), Positives = 56/67 (83%)
+
+Query: 156 TTDRRRRDLGGSAPLAIGLSVALGHLLAIDYTGCGINPARSFGSAVLTRNFSNHWIFWVG 215
+ T D+RR +GGSAPL IG SVALGHL+ I YTGCG+NPARSFG AV+T NF+NHW++WVG
+Sbjct: 22 TDDKRRGSVGGSAPLPIGFSVALGHLIGIPYTGCGMNPARSFGPAVVTGNFTNHWVYWVG 81
+
+Query: 216 PFIGSAL 222
+ P IG+ L
+Sbjct: 82 PIIGAVL 88
+
+ Score = 95 (44.1 bits), Expect = 2.3e-06, P = 2.3e-06
+ Identities = 20/33 (60%), Positives = 23/33 (69%)
+
+Query: 136 GQGLGIEIIGTLQLVLCVLATTDRRRRDLGGSA 168
+ GQ L +EIIGT QLV CV ATTD +RR G +
+Sbjct: 1 GQNLVVEIIGTFQLVYCVFATTDDKRRGSVGGS 33
+
+>45663 p34.2 (1) AQPZ_ECOLI // AQUAPORIN Z.
+ Length = 96
+
+ Score = 90 (41.8 bits), Expect = 3.2e-13, Sum P(2) = 3.2e-13
+ Identities = 18/36 (50%), Positives = 25/36 (69%)
+
+Query: 166 GSAPLAIGLSVALGHLLAIDYTGCGINPARSFGSAV 201
+ G AP+AIGL++ L HL++I T +NPARS A+
+Sbjct: 25 GFAPIAIGLALTLIHLISIPVTNTSVNPARSTAVAI 60
+
+ Score = 63 (29.2 bits), Expect = 3.2e-13, Sum P(2) = 3.2e-13
+ Identities = 11/25 (44%), Positives = 14/25 (56%)
+
+Query: 210 WIFWVGPFIGSALAVLIYDFILAPR 234
+ W FWV P +G + LIY +L R
+Sbjct: 71 WFFWVVPIVGGIIGGLIYRTLLEKR 95
+
+>45611 p34.2 (1) AQP2_HUMAN // AQUAPORIN-CD (AQP-CD) (WATER CHANNEL PROTEIN FOR
+ RENAL COLLECTING DUCT) (ADH WATER CHANNEL) (AQUAPORIN 2) (COLLECTING DUCT
+ WATER CHANNEL PROTEIN) (WCH-CD).
+ Length = 49
+
+ Score = 136 (63.1 bits), Expect = 6.0e-13, P = 6.0e-13
+ Identities = 23/42 (54%), Positives = 34/42 (80%)
+
+Query: 50 VKVSLAFGLSIATLAQSVGHISGAHSNPAVTLGLLLSCQISI 91
+ +++++AFGL I TL Q++GHISGAH NPAVT+ L+ C +S+
+Sbjct: 8 LQIAMAFGLGIGTLVQALGHISGAHINPAVTVACLVGCHVSV 49
+
+>304 p34.2 (61) AQP2(10) GLPF(6) MIP(5) // PROTEIN CHANNEL WATER AQUAPORIN
+ INTRINSIC DUCT COLLECTING FOR TONOPLAST WCH-CD
+ Length = 43
+
+ Score = 121 (56.1 bits), Expect = 9.2e-11, P = 9.2e-11
+ Identities = 24/43 (55%), Positives = 31/43 (72%)
+
+Query: 70 ISGAHSNPAVTLGLLLSCQISILRAVMYIIAQCVGAIVASAIL 112
+ ISG H NPAVT+GLL+ + LRAV YI AQ +GA+ +A+L
+Sbjct: 1 ISGGHINPAVTIGLLIGGRFPFLRAVFYIAAQLLGAVAGAALL 43
+
+>45607 p34.2 (1) PMIP_NICAL // POLLEN-SPECIFIC MEMBRANE INTEGRAL PROTEIN.
+ Length = 69
+
+ Score = 80 (37.1 bits), Expect = 1.2e-07, Sum P(2) = 1.2e-07
+ Identities = 17/54 (31%), Positives = 32/54 (59%)
+
+Query: 149 LVLCVLATTDRRRRDLGGSAPLAIGLSVALGHLLAIDYTGCGINPARSFGSAVL 202
+ L++ V++ R +G A +A+G+++ L +A +G +NPARS G A++
+Sbjct: 13 LLMFVISGVATDDRAIGQVAGIAVGMTITLNVFVAGPISGASMNPARSIGPAIV 66
+
+ Score = 34 (15.8 bits), Expect = 1.2e-07, Sum P(2) = 1.2e-07
+ Identities = 8/18 (44%), Positives = 11/18 (61%)
+
+Query: 136 GQGLGIEIIGTLQLVLCV 153
+ GQ L IEII + L+ +
+Sbjct: 1 GQSLAIEIIISFLLMFVI 18
+
+>45606 p34.2 (1) BIB_DROME // NEUROGENIC PROTEIN BIG BRAIN.
+ Length = 119
+
+ Score = 80 (37.1 bits), Expect = 1.2e-05, Sum P(2) = 1.2e-05
+ Identities = 15/34 (44%), Positives = 24/34 (70%)
+
+Query: 1 MASEIKKKLFWRAVVAEFLAMTLFVFISIGSALG 34
+ M +EI+ FWR++++E LA ++VFI G+A G
+Sbjct: 55 MQAEIRTLEFWRSIISECLASFMYVFIVCGAAAG 88
+
+ Score = 39 (18.1 bits), Expect = 1.2e-05, Sum P(2) = 1.2e-05
+ Identities = 9/17 (52%), Positives = 12/17 (70%)
+
+Query: 53 SLAFGLSIATLAQSVGH 69
+ +LA GL++ATL Q H
+Sbjct: 103 ALASGLAMATLTQCFLH 119
+
+>2027 p34.2 (15) GLPF(9) AQP3(2) // PROTEIN FACILITATOR GLYCEROL UPTAKE
+ AQUAPORIN DIFFUSION UPTAKE/EFFLUX PEPX 5'REGION ORF1
+ Length = 55
+
+ Score = 60 (27.8 bits), Expect = 3.4e-05, Sum P(2) = 3.4e-05
+ Identities = 17/46 (36%), Positives = 20/46 (43%)
+
+Query: 156 TTDRRRRDLGGSAPLAIGLSVALGHLLAIDYTGCGINPARSFGSAV 201
+ T D GG PL +G V + TG INPAR FG +
+Sbjct: 10 TDDGNNVPSGGLHPLMVGFLVMGIGMSLGGTTGYAINPARDFGPRI 55
+
+ Score = 37 (17.2 bits), Expect = 3.4e-05, Sum P(2) = 3.4e-05
+ Identities = 7/10 (70%), Positives = 8/10 (80%)
+
+Query: 149 LVLCVLATTD 158
+ L+ CVLA TD
+Sbjct: 2 LIACVLALTD 11
+
+>45615 p34.2 (1) GLPF_STRPN // GLYCEROL UPTAKE FACILITATOR PROTEIN.
+ Length = 26
+
+ Score = 63 (29.2 bits), Expect = 0.025, P = 0.024
+ Identities = 13/23 (56%), Positives = 18/23 (78%)
+
+Query: 205 NFSNHWIFWVGPFIGSALAVLIY 227
+ ++S WI VGP IG+ALAVL++
+Sbjct: 1 DWSYAWIPVVGPVIGAALAVLVF 23
+
+>45638 p34.2 (1) AQP5_HUMAN // AQUAPORIN 5.
+ Length = 27
+
+ Score = 61 (28.3 bits), Expect = 0.045, P = 0.044
+ Identities = 11/19 (57%), Positives = 18/19 (94%)
+
+Query: 50 VKVSLAFGLSIATLAQSVG 68
+ ++++LAFGL+I TLAQ++G
+Sbjct: 8 LQIALAFGLAIGTLAQALG 26
+
+Parameters:
+ E=0.1
+ B=500
+
+ V=500
+ -ctxfactor=1.00
+
+ Query ----- As Used ----- ----- Computed ----
+ Frame MatID Matrix name Lambda K H Lambda K H
+ +0 0 BLOSUM62 0.322 0.138 0.394 same same same
+
+ Query
+ Frame MatID Length Eff.Length E S W T X E2 S2
+ +0 0 269 269 0.10 69 3 11 22 0.22 33
+
+Statistics:
+ Query Expected Observed HSPs HSPs
+ Frame MatID High Score High Score Reportable Reported
+ +0 0 59 (27.4 bits) 270 (125.3 bits) 14 14
+
+ Query Neighborhd Word Excluded Failed Successful Overlaps
+ Frame MatID Words Hits Hits Extensions Extensions Excluded
+ +0 0 5349 3124825 609708 2510548 4569 2
+
+ Database: /home/phd/ut/prodom/prodom_34_2
+ Release date: unknown
+ Posted date: 12:24 PM MET DST May 06, 1998
+ # of letters in database: 6,740,067
+ # of sequences in database: 53,597
+ # of database sequences satisfying E: 9
+ No. of states in DFA: 564 (111 KB)
+ Total size of DFA: 226 KB (256 KB)
+ Time to generate neighborhood: 0.03u 0.00s 0.03t Real: 00:00:00
+ Time to search database: 9.80u 0.03s 9.83t Real: 00:00:10
+ Total cpu time: 9.90u 0.06s 9.96t Real: 00:00:10
+--- END of BLASTP output
+--- ------------------------------------------------------------
+---
+--- Again: these results were obtained based on the domain data-
+--- base collected by Daniel Kahn and his coworkers in Toulouse.
+---
+--- PLEASE quote:
+--- F Corpet, J Gouzy, D Kahn (1998). The ProDom database
+--- of protein domain families. Nucleic Ac Res 26:323-326.
+---
+--- The general WWW page is on:
+---- ---------------------------------------
+--- http://www.toulouse.inra.fr/prodom.html
+---- ---------------------------------------
+---
+--- For WWW graphic interfaces to PRODOM, in particular for your
+--- protein family, follow the following links (each line is ONE
+--- single link for your protein!!):
+---
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=390 ==> multiple alignment, consensus, PDB and PROSITE links of domain 390
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=390 ==> graphical output of all proteins having domain 390
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=45663 ==> multiple alignment, consensus, PDB and PROSITE links of domain 45663
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=45663 ==> graphical output of all proteins having domain 45663
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=45611 ==> multiple alignment, consensus, PDB and PROSITE links of domain 45611
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=45611 ==> graphical output of all proteins having domain 45611
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=304 ==> multiple alignment, consensus, PDB and PROSITE links of domain 304
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=304 ==> graphical output of all proteins having domain 304
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=45607 ==> multiple alignment, consensus, PDB and PROSITE links of domain 45607
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=45607 ==> graphical output of all proteins having domain 45607
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=45606 ==> multiple alignment, consensus, PDB and PROSITE links of domain 45606
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=45606 ==> graphical output of all proteins having domain 45606
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=2027 ==> multiple alignment, consensus, PDB and PROSITE links of domain 2027
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=2027 ==> graphical output of all proteins having domain 2027
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=45615 ==> multiple alignment, consensus, PDB and PROSITE links of domain 45615
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=45615 ==> graphical output of all proteins having domain 45615
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=45638 ==> multiple alignment, consensus, PDB and PROSITE links of domain 45638
+http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=45638 ==> graphical output of all proteins having domain 45638
+---
+--- NOTE: if you want to use the link, make sure the entire line
+--- is pasted as URL into your browser!
+---
+--- END of PRODOM
+--- ------------------------------------------------------------
+
+________________________________________________________________________________
+
+--- Database used for sequence comparison:
+--- SEQBASE RELEASE 34.0 OF EMBL/SWISS-PROT WITH 59021 SEQUENCES
+
+The alignment that has been used as input to the network is:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+________________________________________________________________________________
+
+--- ------------------------------------------------------------
+--- MAXHOM multiple sequence alignment
+--- ------------------------------------------------------------
+---
+--- MAXHOM ALIGNMENT HEADER: ABBREVIATIONS FOR SUMMARY
+--- ID : identifier of aligned (homologous) protein
+--- STRID : PDB identifier (only for known structures)
+--- PIDE : percentage of pairwise sequence identity
+--- WSIM : percentage of weighted similarity
+--- LALI : number of residues aligned
+--- NGAP : number of insertions and deletions (indels)
+--- LGAP : number of residues in all indels
+--- LSEQ2 : length of aligned sequence
+--- ACCNUM : SwissProt accession number
+--- NAME : one-line description of aligned protein
+---
+--- MAXHOM ALIGNMENT HEADER: SUMMARY
+ID STRID IDE WSIM LALI NGAP LGAP LEN2 ACCNUM NAME
+aqp1_rat 100 100 269 0 0 269 P29975 PROXIMAL TUBULE) (AQUAPOR
+aqp1_mouse 98 99 269 0 0 269 Q02013 PROXIMAL TUBULE) (AQUAPOR
+aqp1_human 93 97 269 0 0 269 P29972 PROXIMAL TUBULE) (AQUAPOR
+aqp1_bovin 90 95 269 1 2 271 P47865 PROXIMAL TUBULE) (AQUAPOR
+aqp1_sheep 90 94 269 2 3 272 P56401 PROXIMAL TUBULE) (AQUAPOR
+aqpa_ranes 78 89 268 2 5 272 P50501 AQUAPORIN FA-CHIP.
+aqp2_dasno 49 73 109 1 7 109 P79164 PROTEIN) (WCH-CD) (FRAGME
+aqp2_bovin 49 73 109 1 7 109 P79099 PROTEIN) (WCH-CD) (FRAGME
+aqp2_canfa 48 72 109 1 7 109 P79144 PROTEIN) (WCH-CD) (FRAGME
+aqp2_rabit 48 73 109 1 7 109 P79213 PROTEIN) (WCH-CD) (FRAGME
+aqp2_elema 47 72 109 1 7 109 P79168 PROTEIN) (WCH-CD) (FRAGME
+aqp2_horse 47 72 109 1 7 109 P79165 PROTEIN) (WCH-CD) (FRAGME
+aqp2_proha 47 73 109 1 7 109 P79229 PROTEIN) (WCH-CD) (FRAGME
+mip_rat 46 73 259 1 7 261 P09011 LENS FIBER MAJOR INTRINSI
+aqp2_oryaf 46 72 109 1 7 109 P79200 PROTEIN) (WCH-CD) (FRAGME
+mip_mouse 46 73 261 1 7 263 P51180 LENS FIBER MAJOR INTRINSI
+mip_ranpi 45 73 261 1 7 263 Q06019 LENS FIBER MAJOR INTRINSI
+mip_bovin 45 73 261 1 7 263 P06624 LENS FIBER MAJOR INTRINSI
+mip_human 45 73 261 1 7 263 P30301 LENS FIBER MAJOR INTRINSI
+mip_chick 45 72 110 1 1 112 P28238 LENS FIBER MAJOR INTRINSI
+aqp5_rat 44 71 262 2 8 265 P47864 AQUAPORIN 5.
+aqp5_human 44 71 262 2 8 265 P55064 AQUAPORIN 5.
+aqp2_human 44 72 261 2 8 271 P41181 PROTEIN) (WCH-CD).
+aqp4_human 43 70 266 2 5 323 P55087 AQUAPORIN 4 (WCH4) (MERCU
+aqp4_rat 43 70 266 2 5 323 P47863 AQUAPORIN 4 (WCH4) (MERCU
+aqp4_mouse 43 69 265 3 6 322 P55088 AQUAPORIN 4 (WCH4) (MERCU
+aqp2_rat 42 71 261 2 8 271 P34080 PROTEIN) (WCH-CD).
+aqp2_mouse 42 71 261 2 8 271 P56402 PROTEIN) (WCH-CD).
+wc2a_arath 42 67 248 4 12 287 P43286 PLASMA MEMBRANE INTRINSIC
+aqp6_human 42 68 260 2 9 282 Q13520 AQUAPORIN 6 (AQUAPORIN-2
+wc2c_arath 41 66 248 4 12 285 P30302 INTRINSIC PROTEIN) (WSI-T
+wc2b_arath 41 66 248 4 12 285 P43287 PLASMA MEMBRANE INTRINSIC
+wc1c_arath 41 65 238 4 10 286 Q08733 (TMP-B).
+wc1b_arath 41 65 238 4 10 286 Q06611 (TMP-A).
+tipw_lyces 40 65 237 4 10 286 Q08451 (RIPENING-ASSOCIATED MEMB
+wc1a_arath 40 64 238 4 10 286 P43285 PLASMA MEMBRANE INTRINSIC
+tipw_pea 40 64 237 4 11 289 P25794 RESPONSIVE PROTEIN 7A).
+tipa_arath 38 64 250 3 9 268 P26587 TONOPLAST INTRINSIC PROTE
+aqua_atrca 38 64 246 4 10 282 P42767 AQUAPORIN.
+dip_antma 38 65 242 2 4 250 P33560 PROBABLE TONOPLAST INTRIN
+aqpz_ecoli 37 59 220 4 17 231 P48838 AQUAPORIN Z (BACTERIAL NO
+tip2_tobac 37 64 242 2 4 250 P24422 TONOPLAST INTRINSIC PROTE
+tip1_tobac 37 64 242 2 4 250 P21653 TONOPLAST INTRINSIC PROTE
+tipg_arath 33 62 241 2 4 251 P25818 TONOPLAST INTRINSIC PROTE
+bib_drome 33 60 260 4 10 700 P23645 NEUROGENIC PROTEIN BIG BR
+tipr_arath 33 62 243 2 4 253 P21652 TONOPLAST INTRINSIC PROTE
+tipa_phavu 33 62 246 2 4 256 P23958 TONOPLAST INTRINSIC PROTE
+tipg_orysa 32 62 240 2 5 250 P50156 TONOPLAST INTRINSIC PROTE
+---
+--- MAXHOM ALIGNMENT: IN MSF FORMAT
+MSF of: /home/phd/server/work/predict_h25873-22040.hssp from: 1 to: 269
+ /home/phd/server/work/predict_h25873-22040.msfRet MSF: 269 Type: P 24-Nov-98 17:44:5 Check: 3448 ..
+
+ Name: predict_h258 Len: 269 Check: 8331 Weight: 1.00
+ Name: aqp1_rat Len: 269 Check: 8331 Weight: 1.00
+ Name: aqp1_mouse Len: 269 Check: 7552 Weight: 1.00
+ Name: aqp1_human Len: 269 Check: 6501 Weight: 1.00
+ Name: aqp1_bovin Len: 269 Check: 7067 Weight: 1.00
+ Name: aqp1_sheep Len: 269 Check: 7582 Weight: 1.00
+ Name: aqpa_ranes Len: 269 Check: 4844 Weight: 1.00
+ Name: aqp2_dasno Len: 269 Check: 8933 Weight: 1.00
+ Name: aqp2_bovin Len: 269 Check: 9649 Weight: 1.00
+ Name: aqp2_canfa Len: 269 Check: 8990 Weight: 1.00
+ Name: aqp2_rabit Len: 269 Check: 8787 Weight: 1.00
+ Name: aqp2_elema Len: 269 Check: 9381 Weight: 1.00
+ Name: aqp2_horse Len: 269 Check: 8993 Weight: 1.00
+ Name: aqp2_proha Len: 269 Check: 8855 Weight: 1.00
+ Name: mip_rat Len: 269 Check: 9773 Weight: 1.00
+ Name: aqp2_oryaf Len: 269 Check: 8554 Weight: 1.00
+ Name: mip_mouse Len: 269 Check: 9723 Weight: 1.00
+ Name: mip_ranpi Len: 269 Check: 5937 Weight: 1.00
+ Name: mip_bovin Len: 269 Check: 1430 Weight: 1.00
+ Name: mip_human Len: 269 Check: 372 Weight: 1.00
+ Name: mip_chick Len: 269 Check: 4658 Weight: 1.00
+ Name: aqp5_rat Len: 269 Check: 9033 Weight: 1.00
+ Name: aqp5_human Len: 269 Check: 6547 Weight: 1.00
+ Name: aqp2_human Len: 269 Check: 6209 Weight: 1.00
+ Name: aqp4_human Len: 269 Check: 2589 Weight: 1.00
+ Name: aqp4_rat Len: 269 Check: 4412 Weight: 1.00
+ Name: aqp4_mouse Len: 269 Check: 2845 Weight: 1.00
+ Name: aqp2_rat Len: 269 Check: 5748 Weight: 1.00
+ Name: aqp2_mouse Len: 269 Check: 6526 Weight: 1.00
+ Name: wc2a_arath Len: 269 Check: 4866 Weight: 1.00
+ Name: aqp6_human Len: 269 Check: 9404 Weight: 1.00
+ Name: wc2c_arath Len: 269 Check: 6187 Weight: 1.00
+ Name: wc2b_arath Len: 269 Check: 7328 Weight: 1.00
+ Name: wc1c_arath Len: 269 Check: 8575 Weight: 1.00
+ Name: wc1b_arath Len: 269 Check: 9544 Weight: 1.00
+ Name: tipw_lyces Len: 269 Check: 9283 Weight: 1.00
+ Name: wc1a_arath Len: 269 Check: 598 Weight: 1.00
+ Name: tipw_pea Len: 269 Check: 9253 Weight: 1.00
+ Name: tipa_arath Len: 269 Check: 6544 Weight: 1.00
+ Name: aqua_atrca Len: 269 Check: 2848 Weight: 1.00
+ Name: dip_antma Len: 269 Check: 9619 Weight: 1.00
+ Name: aqpz_ecoli Len: 269 Check: 5641 Weight: 1.00
+ Name: tip2_tobac Len: 269 Check: 490 Weight: 1.00
+ Name: tip1_tobac Len: 269 Check: 622 Weight: 1.00
+ Name: tipg_arath Len: 269 Check: 3231 Weight: 1.00
+ Name: bib_drome Len: 269 Check: 7687 Weight: 1.00
+ Name: tipr_arath Len: 269 Check: 4476 Weight: 1.00
+ Name: tipa_phavu Len: 269 Check: 5563 Weight: 1.00
+ Name: tipg_orysa Len: 269 Check: 3537 Weight: 1.00
+
+//
+
+ 1 50
+predict_h258 MASEIKKKLF WRAVVAEFLA MTLFVFISIG SALGFNYPLE RNQTLVQDNV
+aqp1_rat MASEIKKKLF WRAVVAEFLA MTLFVFISIG SALGFNYPLE RNQTLVQDNV
+aqp1_mouse MASEIKKKLF WRAVVAEFLA MTLFVFISIG SALGFNYPLE RNQTLVQDNV
+aqp1_human MASEFKKKLF WRAVVAEFLA TTLFVFISIG SALGFKYPVG NNQTAVQDNV
+aqp1_bovin MASEFKKKLF WRAVVAEFLA MILFIFISIG SALGFHYPIK SNQTtvQDNV
+aqp1_sheep MASEFKKKLF WRAVVAEFLA MILFIFISIG SALGFHYPIK SNQTtvQDNV
+aqpa_ranes MASEFKKKAF WRAVIAEFLA MILFVFISIG AALGFNFPIE EKANQtqDIV
+aqp2_dasno ......SVAF SRAVLAEFLA TLIFVFFGLG SALSWPQALP S.......VL
+aqp2_bovin ......SIAF SRAVLAEFLA TLLFVFFGLG SALNWPQALP S.......VL
+aqp2_canfa ......SVAF SRAVFAEFLA TLLFVFFGLG SALNWPQALP S.......VL
+aqp2_rabit ......SIAF SRAVFAEFLA TLLFVFFGLG SALNWPSALP S.......TL
+aqp2_elema ......SIAF SRAVFSEFLA TLLFVFFGLG SALNWPQALP S.......VL
+aqp2_horse ......SIAF SRAVLAEFLA TLLFVFFGLG SALNWPQAMP S.......VL
+aqp2_proha ......SIAF SRAVLSEFLA TLLFVFFGLG SALNWPQALP S.......VL
+mip_rat ...ELRSASF WRAIFAEFFA TLFYVFFGLG SSLRWA.... ...PGPLHVL
+aqp2_oryaf ......SIAF SKAVFSEFLA TLLFVFFGLG SALNWPQALP S.......GL
+mip_mouse .MWELRSASF WRAIFAEFFA TLFYVFFGLG ASLRWA.... ...PGPLHVL
+mip_ranpi .MWEFRSFSF WRAVFAEFFG TMFYVFFGLG ASLKWAAGPA .......NVL
+mip_bovin .MWELRSASF WRAICAEFFA SLFYVFFGLG ASLRWA.... ...PGPLHVL
+mip_human .MWELRSASF WRAIFAEFFA TLFYVFFGLG SSLRWA.... ...PGPLHVL
+mip_chick .......... .......... .......... .......... ..........
+aqp5_rat MKKEVCSLAF FKAVFAEFLA TLIFVFFGLG SALKWPSALP T.......IL
+aqp5_human MKKEVCSVAF LKAVFAEFLA TLIFVFFGLG SALKWPSALP T.......IL
+aqp2_human .MWELRSIAF SRAVFAEFLA TLLFVFFGLG SALNWPQALP S.......VL
+aqp4_human AFKGVWTQAF WKAVTAEFLA MLIFVLLSLG STINWG...G TEKPLPVDMV
+aqp4_rat AFKGVWTQAF WKAVTAEFLA MLIFVLLSVG STINWG...G SENPLPVDMV
+aqp4_mouse AFKGVWTQAF WKAVSAEFLA TLIFVL.GVG STINWG...G SENPLPVDMV
+aqp2_rat .MWELRSIAF SRAVLAEFLA TLLFVFFGLG SALQWASSPP S.......VL
+aqp2_mouse .MWELRSIAY CRAVLAEFLA TLLFVFFGLG SALQWASSPP S.......VL
+wc2a_arath DGAELKKWSF YRAVIAEFVA TLLFLYITVL TVIGYKIQSD TDAGGVdgIL
+aqp6_human MLACRLWKAI SRALFAEFLA TGLYVFFGVG SVMRWPTALP S.......VL
+wc2c_arath DAEELTKWSL YRAVIAEFVA TLLFLYVTVL TVIGYKIQSD TKAGGVdgIL
+wc2b_arath DADELTKWSL YRAVIAEFVA TLLFLYITVL TVIGYKIQSD TKAGGVdgIL
+wc1c_arath EPGELSSWSF YRAGIAEFIA TFLFLYITVL TVMGVKRA.. PNMCASVGIQ
+wc1b_arath EPGELASWSF WRAGIAEFIA TFLFLYITVL TVMGVKR..S PNMCASVGIQ
+tipw_lyces EPGELSSWSF YRAGIAEFMA TFLFLYITIL TVMGLKRSDS LCSSV..GIQ
+wc1a_arath EPGELSSWSF WRAGIAEFIA TFLFLYITVL TVMGVKR..S PNMCASVGIQ
+tipw_pea EPSELTSWSF YRAGIAEFIA TFLFLYITVL TVMGVVRESS KCKTV..GIQ
+tipa_arath RADEATHPDS IRATLAEFLS TFVFVFAAEG SILSLDKLYW EHAAHAGTni
+aqua_atrca DMGELKLWSF WRAAIAEFIA TLLFLYITVA TVIGYKKETD PCASVGL..L
+dip_antma SIGDSFSVAS IKAYVAEFIA TLLFVFAGVG SAIAYNKLTS DAALDPAGLV
+aqpz_ecoli .........M FRKLAAECFG TFWLVFGGCG SAVLAAGFPE ....LGIGFA
+tip2_tobac SIGDSFSVGS LKAYVAEFIA TLLFVFAGVG SAIAYNKLTA DAALDPAGLV
+tip1_tobac SIGDSFSVGS LKAYVAEFIA TLLFVFAGVG SAIAYNKLTA DAALDPAGLV
+tipg_arath RPDEATRPDA LKAALAEFIS TLIFVVAGSG SGMAFNKLTE NGATTPSGLV
+bib_drome MQAEIRTLEF WRSIISECLA SFMYVFIVCG AAAGVGVGAS VSSVL....L
+tipr_arath RPDEATRPDA LKAALAEFIS TLIFVVAGSG SGMAFNKLTE NGATTPSGLV
+tipa_phavu RTDEATHPDS MRASLAEFAS TFIFVFAGEG SGLALVKIYQ DSAFSAGELL
+tipg_orysa SHQEVYHPGA LKAALAEFIS TLIFVFAGQG SGMAFSKLTG GGATTPAGLI
+
+ 51 100
+predict_h258 KVSLAFGLSI ATLAQSVGHI SGAHSNPAVT LGLLLSCQIS ILRAVMYIIA
+aqp1_rat KVSLAFGLSI ATLAQSVGHI SGAHSNPAVT LGLLLSCQIS ILRAVMYIIA
+aqp1_mouse KVSLAFGLSI ATLAQSVGHI SGAHLNPAVT LGLLLSCQIS ILRAVMYIIA
+aqp1_human KVSLAFGLSI ATLAQSVGHI SGAHLNPAVT LGLLLSCQIS IFRALMYIIA
+aqp1_bovin KVSLAFGLSI ATLAQSVGHI SGAHLNPAVT LGLLLSCQIS VLRAIMYIIA
+aqp1_sheep KVSLAFGLSI ATLAQSVGHI SGAHLNPAVT LGLLLSCQIS ILRAIMYIIA
+aqpa_ranes KVSLAFGISI ATMAQSVGHV SGAHLNPAVT LGCLLSCQIS ILKAVMYIIA
+aqp2_dasno QIALAFGLAI GTLVQALGHV SGAHINPAVT VACLVGCHVS FLRAAFYVAA
+aqp2_bovin QIAMAFGLAI GTLVQALGHV SGAHINPAVT VACLVGCHVS FLRAVFYVAA
+aqp2_canfa QIAMAFGLGI GTLVQALGHV SGAHINPAVT VACLVGCHVS FLRAAFYVAA
+aqp2_rabit QIAMAFGLGI GTLVQALGHV SGAHINPAVT VACLVGCHVS FLRAAFYVAA
+aqp2_elema QIAMAFGLAI GTLVQTLGHI SGAHINPAVT VACLVGCHVS FLRATFYLAA
+aqp2_horse QIAMAFGLAI GTLVQALGHV SGAHINPAVT VACLVGCHVS FLRAAFYVAA
+aqp2_proha QIAMAFGLAI GTLVQTLGHI SGAHINPAVT IACLVGCHVS FLRALFYLAA
+mip_rat QVALAFGLAL ATLVQTVGHI SGAHVNPAVT FAFLVGSQMS LLRAFCYIAA
+aqp2_oryaf QIAMAFGLAI GTLVQTLGHI SGAHINPAVT VACLVGCHVS FLRAIFYVAA
+mip_mouse QVALAFGLAL ATLVQTVGHI SGAHVNPAVT FAFLVGSQMS LLRAFCYIAA
+mip_ranpi VIALAFGLVL ATMVQSIGHV SGAHINPAVT FAFLIGSQMS LFRAIFYIAA
+mip_bovin QVALAFGLAL ATLVQAVGHI SGAHVNPAVT FAFLVGSQMS LLRAICYMVA
+mip_human QVAMAFGLAL ATLVQSVGHI SGAHVNPAVT FAFLVGSQMS LLRAFCYMAA
+mip_chick .......... .......... .......... .......... ..........
+aqp5_rat QISIAFGLAI GTLAQALGPV SGGHINPAIT LALLIGNQIS LLRAVFYVAA
+aqp5_human QIALAFGLAI GTLAQALGPV SGGHINPAIT LALLVGNQIS LLRAFFYVAA
+aqp2_human QIAMAFGLGI GTLVQALGHI SGAHINPAVT VACLVGCHVS VLRAAFYVAA
+aqp4_human LISLCFGLSI ATMVQCFGHI SGGHINPAVT VAMVCTRKIS IAKSVFYIAA
+aqp4_rat LISLCFGLSI ATMVQCFGHI SGGHINPAVT VAMVCTRKIS IAKSVFYITA
+aqp4_mouse LISLCFGLSI ATMVQCLGHI SGGHINPAVT VAMVCTRKIS IAKSVFYIIA
+aqp2_rat QIAVAFGLGI GILVQALGHV SGAHINPAVT VACLVGCHVS FLRAAFYVAA
+aqp2_mouse QIAVAFGLGI GTLVQALGHV SGAHINPAVT VACLVGCHVS FLRAAFYVAA
+wc2a_arath GIAWAFGGMI FILVYCTAGI SGGHINPAVT FGLFLARKVS LPRALLYIIA
+aqp6_human QIAITFNLVT AMAVQVTWKT SGAHANPAVT LAFLVGSHIS LPRAVAYVAA
+wc2c_arath GIAWAFGGMI FILVYCTAGI SGGHINPAVT FGLFLARKVS LIRAVLYMVA
+wc2b_arath GIAWAFGGMI FILVYCTAGI SGGHINPAVT FGLFLARKVS LIRAVLYMVA
+wc1c_arath GIAWAFGGMI FALVYCTAGI SGGHINPAVT FGLFLARKLS LTRAVFYIVM
+wc1b_arath GIAWAFGGMI FALVYCTAGI SGGHINPAVT FGLFLARKLS LTRAVYYIVM
+tipw_lyces GVAWAFGGMI FALVYCTAGI SGGHINPAVT FGLFLARKLS LTRAVFYMVM
+wc1a_arath GIAWAFGGMI FALVYCTAGI SGGHINPAVT FGLFLARKLS LTRALYYIVM
+tipw_pea GIAWAFGGMI FALVYCTAGI SGGHINPAVT FGLFLARKLS LTRAIFYMVM
+tipa_arath LVALAHAFAL FAAVSAAINV SGGHVNPAVT FGALVGGRVT AIRAIYYWIA
+aqua_atrca GIAWSFGGMI FVLVYCTAGI SGGHINPAVT FGLFLARKVS LLRALVYMIA
+dip_antma AVAVAHAFAL FVGVSMAANV SGGHLNPAVT LGLAVGGNIT ILTGLFYWIA
+aqpz_ecoli GVALAFGLTV LTMAFAVGHI SGGHFNPAVT IGLWAGGRFP AKEVVGYVIA
+tip2_tobac AVAVAHAFAL FVGVSIAANI SGGHLNPAVT LGLAVGGNIT ILTGFFYWIA
+tip1_tobac AVAVAHAFAL FVGVSIAANI SGGHLNPAVT LGLAVGGNIT ILTGFFYWIA
+tipg_arath AAAVAHAFGL FVAVSVGANI SGGHVNPAVT FGAFIGGNIT LLRGILYWIA
+bib_drome ATALASGLAM ATLTQCFLHI SGAHINPAVT LALCVVRSIS PIRAAMYITA
+tipr_arath AAAVAHAFGL FVAVSVGANI SGGHVNPAVT FGAFIGGNIT LLRGILYWIA
+tipa_phavu ALALAHAFAL FAAVSASMHV SGGHVNPAVS FGALIGGRIS VIRAVYYWIA
+tipg_orysa AAAVAHAFAL FVAVSVGANI SGGHVNPAVT FGAFVGGNIT LFRGLLYWIA
+
+ 101 150
+predict_h258 QCVGAIVASA ILSGITSSLL ENSLGRNDLA RGVNSGQGLG IEIIGTLQLV
+aqp1_rat QCVGAIVASA ILSGITSSLL ENSLGRNDLA RGVNSGQGLG IEIIGTLQLV
+aqp1_mouse QCVGAIVATA ILSGITSSLV DNSLGRNDLA HGVNSGQGLG IEIIGTLQLV
+aqp1_human QCVGAIVATA ILSGITSSLT GNSLGRNDLA DGVNSGQGLG IEIIGTLQLV
+aqp1_bovin QCVGAIVATA ILSGITSSLP DNSLGLNALA PGVNSGQGLG IEIIGTLQLV
+aqp1_sheep QCVGAIVATV ILSGITSSLP DNSLGLNALA PGVNSGQGLG IEIIGTLQLV
+aqpa_ranes QCLGAVVATA ILSGITSGLE NNSLGLNGLS PGVSAGQGLG VEILVTFQLV
+aqp2_dasno QLLGAVAGAA ILHEITPPDV RG........ .......... ..........
+aqp2_bovin QLLGAVAGAA LLHEITPPAI RG........ .......... ..........
+aqp2_canfa QLLGAVAGAA LLHEITPPHV RG........ .......... ..........
+aqp2_rabit QLLGAVAGAA LLHEITPAEV RG........ .......... ..........
+aqp2_elema QLLGAVAGAA LLHELTPPDI RG........ .......... ..........
+aqp2_horse QLLGAVAGAA LLHEITPPDI RR........ .......... ..........
+aqp2_proha QLLGAVAGAA LLHELTPPDI RG........ .......... ..........
+mip_rat QLLGAVAGAA VLYSVTPPAV RGNLALNTLH AGVSVGQATT VEIFLTLQFV
+aqp2_oryaf QLLGAVAGAA LLHELTPPDI RG........ .......... ..........
+mip_mouse QLLGAVAGAA VLYSVTPPAV RGNLALNTLH TGVSVGQATT VEIFLTLQFV
+mip_ranpi QLLGAVAGAA VLYGVTPAAI RGNLALNTLH PGVSLGQATT VEIFLTLQFV
+mip_bovin QLLGAVAGAA VLYSVTPPAV RGNLALNTLH PGVSVGQATI VEIFLTLQFV
+mip_human QLLGAVAGAA VLYSVTPPAV RGNLALNTLH PAVSVGQATT VEIFLTLQFV
+mip_chick .......... .......... .......... .......... ..........
+aqp5_rat QLVGAIAGAG ILYWLAPLNA RGNLAVNALN NNTTPGKAMV VELILTFQLA
+aqp5_human QLVGAIAGAG ILYGVAPLNA RGNLAVNALN NNTTQGQAMV VELILTFQLA
+aqp2_human QLLGAVAGAA LLHEITPADI RGDLAVNALS NSTTAGQAVT VELFLTLQLV
+aqp4_human QCLGAIIGAG ILYLVTPPSV VGGLGVTMVH GNLTAGHGLL VELIITFQLV
+aqp4_rat QCLGAIIGAG ILYLVTPPSV VGGLGVTTVH GNLTAGHGLL VELIITFQLV
+aqp4_mouse QCLGAIIGAG ILYLVTPPSV VGGLGVTTVH GNLTAGHGLL VELIITFQLV
+aqp2_rat QLLGAVAGAA ILHEITPVEI RGDLAVNALH NNATAGQAVT VELFLTMQLV
+aqp2_mouse QLLGAVAGAA ILHEITPVEI RGDLAVNALH NNATAGQAVT VELFLTMQLV
+wc2a_arath QCLGAICGVG FVKAFQSSYY TRYGGgnSLA DGYSTGTGLA AEIIGTFVLV
+aqp6_human QLVGATVGAA LLYGVMPGDI RETLGINVVR NSVSTGQAVA VELLLTLQLV
+wc2c_arath QCLGAICGVG FVKAFQSSHY VNYGGgnFLA DGYNTGTGLA AEIIGTFVLV
+wc2b_arath QCLGAICGVG FRQSFQSSYY DRYGGgnSLA DGYNTGTGLA AEIIGTFVLV
+wc1c_arath QCLGAICGAG VVKGFQPNPY QtgGGANTVA HGYTKGSGLG AEIIGTFVLV
+wc1b_arath QCLGAICGAG VVKGFQPKQY QagGGANTIA HGYTKGSGLG AEIIGTFVLV
+tipw_lyces QCLGAICGAG VVKGFMVGPY QrgGGANVVN PGYTKGDGLG AEIIGTFVLV
+wc1a_arath QCLGAICGAG VVKGFQPKQY QagGGANTVA HGYTKGSGLG AEIIGTFVLV
+tipw_pea QVLGAICGAG VVKGFEGKQR FGDLNgnFVA PGYTKGDGLG AEIVGTFILV
+tipa_arath QLLGAILACL LLRLTTNGMR PVGFR...LA SGVGAVNGLV LEIILTFGLV
+aqua_atrca QCAGAICGVG LVKAFMKGPY NqgGGANSVA LGYNKGTAFG AELIGTFVLV
+dip_antma QCLGSTVACL LLKFVTNGL. ..SVPTHGVA AGMDAIQGVV MEIIITFALV
+aqpz_ecoli QVVGGIVAAA LLYLIASGKT GFDAAASGFA sgYSMLSALV VELVLSAGFL
+tip2_tobac QLLGSTVACL LLKYVTNGL. ..AVPTHGVA AGLNGFQGVV MEIIITFALV
+tip1_tobac QLLGSTVACL LLKYVTNGL. ..AVPTHGVA AGLNGLQGVV MEIIITFALV
+tipg_arath QLLGSVVACL ILKFATGGLA VPAFG...LS AGVGVLNAFV FEIVMTFGLV
+bib_drome QCGGGIAGAA LLYGVTVPGY QGNLQAasHS AALAAWERFG VEFILTSLVV
+tipr_arath QLLGSVVACL ILKFATGGLA VPPFG...LS AGVGVLNAFV FEIVMTFGLV
+tipa_phavu QLLGSIVAAL VLRLVTNNMR PSGF...HVS PGVGVGHMFI LEVVMTFGLM
+tipg_orysa QLLGSTVACF LLRFSTGGLA TGTFGL.... TGVSVWEALV LEIVMTFGLV
+
+ 151 200
+predict_h258 LCVLATTDRR RRDLGGSAPL AIGLSVALGH LLAIDYTGCG INPARSFGSA
+aqp1_rat LCVLATTDRR RRDLGGSAPL AIGLSVALGH LLAIDYTGCG INPARSFGSA
+aqp1_mouse LCVLATTDRR RRDLGGSAPL AIGLSVALGH LLAIDYTGCG INPARSFGSA
+aqp1_human LCVLATTDRR RRDLGGSAPL AIGLSVALGH LLAIDYTGCG INPARSFGSA
+aqp1_bovin LCVLATTDRR RRDLGGSGPL AIGFSVALGH LLAIDYTGCG INPARSFGSS
+aqp1_sheep LCVLATTDRR RrdLGDSGPL AIGFSVALGH LLAIDYTGCG INPARSFGSS
+aqpa_ranes LCVVAVTDRR RHDVSGSVPL AIGLSVALGH LIAIDYTGCG MNPARSFGSA
+aqp2_dasno .......... .......... .......... .......... ..........
+aqp2_bovin .......... .......... .......... .......... ..........
+aqp2_canfa .......... .......... .......... .......... ..........
+aqp2_rabit .......... .......... .......... .......... ..........
+aqp2_elema .......... .......... .......... .......... ..........
+aqp2_horse .......... .......... .......... .......... ..........
+aqp2_proha .......... .......... .......... .......... ..........
+mip_rat LCIFATYDER RNGRMGSVAL AVGFSLTLGH LFGMYYTGAG MNPARSFAPA
+aqp2_oryaf .......... .......... .......... .......... ..........
+mip_mouse LCIFATYDER RNGRMGSVAL AVGFSLTLGH LFGMYYTGAG MNPARSFAPA
+mip_ranpi LCIFATYDER RNGRLGSVSL AIGFSLTLGH LFGLYYTGAS MNPARSFAPA
+mip_bovin LCIFATYDER RNGRLGSVAL AVGFSLTLGH LFGMYYTGAG MNPARSFAPA
+mip_human LCIFATYDER RNGQLGSVAL AVGFSLALGH LFGMYYTGAG MNPARSFAPA
+mip_chick ........DR HDGRPGSAAL PVGFSLALGH LFGIPFTGAG MNPARSFAPA
+aqp5_rat LCIFSSTDSR RTSPVGSPAL SIGLSVTLGH LVGIYFTGCS MNPARSFGPA
+aqp5_human LCIFASTDSR RTSPVGSPAL SIGLSVTLGH LVGIYFTGCS MNPARSFGPA
+aqp2_human LCIFASTDER RGENPGTPAL SIGFSVALGH LLGIHYTGCS MNPARSLAPA
+aqp4_human FTIFASCDSK RTDVTGSIAL AIGFSVAIGH LFAINYTGAS MNPARSFGPA
+aqp4_rat FTIFASCDSK RTDVTGSVAL AIGFSVAIGH LFAINYTGAS MNPARSFGPA
+aqp4_mouse FTVFASCDSK RTDVTGSIAL AIGFSVAIGH LFAINYTGAS MNPARSFGPA
+aqp2_rat LCIFASTDER RGDNLGSPAL SIGFSVTLGH LLGIYFTGCS MNPARSLAPA
+aqp2_mouse LCIFASTDER RSDNLGSPAL SIGFSVTLGH LLGIYFTGCS MNPARSLAPA
+wc2a_arath YTVFSATDPK RSavPVLAPL PIGFAVFMVH LATIPITGTG INPARSFGAA
+aqp6_human LCVFASTDSR QTS..GSPAT MIGISWALGH LIGILFTGCS MNPARSFGPA
+wc2c_arath YTVFSATDPK RNavPVLAPL PIGFAVFMVH LATIPITGTG INPARSFGAA
+wc2b_arath YTVFSATDPK RNavPVLAPL PIGFAVFMVH LATIPITGTG INPARSFGAS
+wc1c_arath YTVFSATDAK RSavPILAPL PIGFAVFLVH LATIPITGTG INPARSLGAA
+wc1b_arath YTVFSATDAK RNavPILAPL PIGFAVFLVH LATIPITGTG INPARSLGAA
+tipw_lyces YTVFSATDAK RNavPILAPL PIGFAVFLVH LATIPITGTG INPARSLGAA
+wc1a_arath YTVFSATDAK RNavPILAPL PIGFAVFLVH LATIPITATG INPARSLGAA
+tipw_pea YTVFSATDAK RSavPILAPL PIGFAVFLVH LATIPITGTG INPARSLGAA
+tipa_arath YVVYStiDPK RGSLGIIAPL AIGLIVGANI LVGGPFSGAS MNPARAFGPA
+aqua_atrca YTVFSATDPK RSavPILAPL PIGFAVFMVH LATIPITGTG INPARSFGAA
+dip_antma YTVYAtaDPK KGSLGVIAPI AIGFIVGANI LAAGPFSGGS MNPARSFGPA
+aqpz_ecoli LVIHGATDKF APA..GFAPI AIGLALTLIH LISIPVTNTS VNPARSTAVA
+tip2_tobac YTVYAtaDPK KGSLGTIAPI AIGFIVGANI LAAGPFSGGS MNPARSFGPA
+tip1_tobac YTVYAtaDPK KGSLGTIAPI AIGFIVGANI LAAGPFSGGS MNPARSFGPA
+tipg_arath YTVYAtiDPK NGSLGTIAPI AIGFIVGANI LAGGAFSGAS MNPAVAFGPA
+bib_drome LCYFVSTDPM KKFMGNS.AA SIGCAYSACC FVSMPYLN.. ..PARSLGPS
+tipr_arath YTVYAtiDPK NGSLGTIAPI AIGFIVGANI LAGGAFSGAS MNPAVAFGPA
+tipa_phavu YTVYGtiDPK RGAVSYIAPL AIGLIVGANI LVGGPFDGAC MNPALAFGPS
+tipg_orysa YTVYAtvDPK KGSLGTIAPI AIGFIVGANI LVGGAFDGAS MNPAVSFGPA
+
+ 201 250
+predict_h258 VLTRNFSNHW IFWVGPFIGS ALAVLIYDFI LAPRSSDFTD RMKVWTSGQV
+aqp1_rat VLTRNFSNHW IFWVGPFIGS ALAVLIYDFI LAPRSSDFTD RMKVWTSGQV
+aqp1_mouse VLTRNFSNHW IFWVGPFIGG ALAVLIYDFI LAPRSSDFTD RMKVWTSGQV
+aqp1_human VITHNFSNHW IFWVGPFIGG ALAVLIYDFI LAPRSSDLTD RVKVWTSGQV
+aqp1_bovin VITHNFQDHW IFWVGPFIGA ALAVLIYDFI LAPRSSDLTD RVKVWTSGQV
+aqp1_sheep VITHNFQDHW IFWVGPFIGA ALAVLIYDFI LAPRSSDLTD RVKVWTSGQV
+aqpa_ranes VLTKNFTYHW IFWVGPMIGG AAAAIIYDFI LAPRTSDLTD RMKVWTNGQV
+aqp2_dasno .......... .......... .......... .......... ..........
+aqp2_bovin .......... .......... .......... .......... ..........
+aqp2_canfa .......... .......... .......... .......... ..........
+aqp2_rabit .......... .......... .......... .......... ..........
+aqp2_elema .......... .......... .......... .......... ..........
+aqp2_horse .......... .......... .......... .......... ..........
+aqp2_proha .......... .......... .......... .......... ..........
+mip_rat ILTRNFSNHW VYWVGPIIGG GLGSLLYDFL LFPRLKSVSE RLSILKGARP
+aqp2_oryaf .......... .......... .......... .......... ..........
+mip_mouse ILTRNFSNHW VYWVGPIIGG GLGSLLYDFL LFPRLKSVSE RLSILKGARP
+mip_ranpi VLTRNFTNHW VYWVGPIIGG ALGGLVYDFI LFPRMRGLSE RLSILKGARP
+mip_bovin ILTRNFTNHW VYWVGPVIGA GLGSLLYDFL LFPRLKSVSE RLSILKGSRP
+mip_human ILTGNFTNHW VYWVGPIIGG GLGSLLYDFL LFPRLKSISE RLSVLKGAKP
+mip_chick VITRNFTNHW VFWAGPLLGA ALAALLYELA LCPRARSMAE RLAV.LRGEP
+aqp5_rat VVMNRFssHW VFWVGPIVGA MLAAILYFYL LFPSSLSLHD RVAVVKGTYE
+aqp5_human VVMNRFsaHW VFWVGPIVGA VLAAILYFYL LFPNSLSLSE RVAIIKGTYE
+aqp2_human VVTGKFDDHW VFWIGPLVGA ILGSLLYNYV LFPPAKSLSE RLAVLKGLEp
+aqp4_human VIMGNWENHW IYWVGPIIGA VLAGGLYEYV FCPDVEFKRR FKEAFSKaqT
+aqp4_rat VIMGNWENHW IYWVGPIIGA VLAGALYEYV FCPDVELKRR LKEAFSKaqT
+aqp4_mouse VIMGNWANHW IYWVGPIMGA VLAGALYEYV FCPDVELKRR LKEAFSKaqT
+aqp2_rat VVTGKFDDHW VFWIGPLVGA IIGSLLYNYL LFPSAKSLQE RLAVLKGLEp
+aqp2_mouse VVTGKFDDHW VFWIGPLVGA IIGSLLYNYL LFPSTKSLQE RLAVLKGLEp
+wc2a_arath VIYnpWDDHW IFWVGPFIGA AIAAFYHQFV LRASGSKSLG SFRSAANV..
+aqp6_human IIIGKFTVHW VFWVGPLMGA LLASLIYNFV LFPDTKTLAQ RLAILTGTVE
+wc2c_arath VIFnpWDDHW IFWVGPFIGA TIAAFYHQFV LRASGSKSLG SFRSAANV..
+wc2b_arath VIYnpWDDHW IFWVGPFIGA AIAAFYHQFV LRASGSKSLG SFRSAANV..
+wc1c_arath IIYnaWDDHW IFWVGPFIGA ALAALYHQLV IRAIPFKSRS ..........
+wc1b_arath IIFnaWDDHW VFWVGPFIGA ALAALYHVIV IRAIPFKSRS ..........
+tipw_lyces IIYnaWNDHW IFWVGPMIGA ALAAIYHQII IRAMPFHRS. ..........
+wc1a_arath IIYnsWDDHW VFWVGPFIGA ALAALYHVVV IRAIPFKSRS ..........
+tipw_pea IVFngWNDHW IFWVGPFIGA ALAALYHQVV IRAIPFKSK. ..........
+tipa_arath LVGWRWHDHW IYWVGPFIGS ALAALIYEYM VIPTEPPTHH AHGVHQPLAP
+aqua_atrca VIyrVWDDHW IFWVGPFVGA LAAAAYHQYV LRAAAIKALG SFRSNPTN..
+dip_antma VASGDFSQNW IYWAGPLIGG ALAGFIYGDV FITAHAPLPT SEDYA.....
+aqpz_ecoli IFQgaLEQLW FFWVVPIVGG IIGGLIYRTL LEKRD..... ..........
+tip2_tobac VVAGDFSQNW IYWAGPLIGG GLAGFIYGDV FIGCHTPLPT SEDYA.....
+tip1_tobac VVAGDFSQNW IYWAGPLIGG GLAGFIYGDV FIGCHTPLPT SEDYA.....
+tipg_arath VVSWTWTNHW VYWAGPLVGG GIAGLIYEVF FINTTHEQLP TTDY......
+bib_drome FVLNKWDSHW VYWFGPLVGG MASGLVYEYI FNSRNRNLRH NKGSIDNDSS
+tipr_arath VVSWTWTNHW VYWAGPLVGG GIAGLIYEVF FINTTHTSSS NHRLLN....
+tipa_phavu LVGWQWHQHW IFWVGPLLGA ALAALVYEYA VIPIEPPPHH HQPLATEDY.
+tipg_orysa LVSWSWESQW VYWVGPLIGG GLAGVIYEVL FISHTHEQLP TTDY......
+
+ 251 269
+predict_h258 EEYDLDADDI NSRVEMKPK
+aqp1_rat EEYDLDADDI NSRVEMKPK
+aqp1_mouse EEYDLDADDI NSRVEMKPK
+aqp1_human EEYDLDADDI NSRVEMKPK
+aqp1_bovin EEYDLDADDI NSRVEMKPK
+aqp1_sheep EEYDLDADDI NSRVEMKPK
+aqpa_ranes EEYELDGDD. NTRVEMKPK
+aqp2_dasno .......... .........
+aqp2_bovin .......... .........
+aqp2_canfa .......... .........
+aqp2_rabit .......... .........
+aqp2_elema .......... .........
+aqp2_horse .......... .........
+aqp2_proha .......... .........
+mip_rat SDSNGQPEGT GEPVELKTQ
+aqp2_oryaf .......... .........
+mip_mouse SDSNGQPEGT GEPVELKTQ
+mip_ranpi AEPEGQQEAT GEPIELKTQ
+mip_bovin SESNGQPEVT GEPVELKTQ
+mip_human DVSNGQPEVT GEPVELNTQ
+mip_chick PAAAPPPEPP AEPLELKTQ
+aqp5_rat PEEDWEDHRE ERKKTIELT
+aqp5_human PDEDWEEQRE ERKKTMELT
+aqp2_human tDWEEREVRR RQSVELHSP
+aqp4_human KGSYMEVEDN RSQVETDDL
+aqp4_rat KGSYMEVEDN RSQVETEDL
+aqp4_mouse KGSYMEVEDN RSQVETEDL
+aqp2_rat tDWEEREVRR RQSVELHSP
+aqp2_mouse tDWEEREVRR RQSVELHSP
+wc2a_arath .......... .........
+aqp6_human VGTGARAGAE PLKKESQPG
+wc2c_arath .......... .........
+wc2b_arath .......... .........
+wc1c_arath .......... .........
+wc1b_arath .......... .........
+tipw_lyces .......... .........
+wc1a_arath .......... .........
+tipw_pea .......... .........
+tipa_arath EDY....... .........
+aqua_atrca .......... .........
+dip_antma .......... .........
+aqpz_ecoli .......... .........
+tip2_tobac .......... .........
+tip1_tobac .......... .........
+tipg_arath .......... .........
+bib_drome SIHSEDELNY DMDMEKPNK
+tipr_arath .......... .........
+tipa_phavu .......... .........
+tipg_orysa .......... .........
+
+________________________________________________________________________________
+
+ Prediction of:
+
+ - secondary structure, by PHDsec
+ - solvent accessibility, by PHDacc
+ - and helical transmembrane regions, by PHDhtm
+
+ PHD: Profile fed neural network systems from HeiDelberg
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+ Author: Burkhard Rost
+ EMBL, Heidelberg, FRG
+ Meyerhofstrasse 1, 69 117 Heidelberg
+ Internet: Predict-Help@EMBL-Heidelberg.DE
+
+ All rights reserved.
+
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ Secondary structure prediction by PHDsec:
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+ Author: Burkhard Rost
+ EMBL, Heidelberg, FRG
+ Meyerhofstrasse 1, 69 117 Heidelberg
+ Internet: Rost@EMBL-Heidelberg.DE
+
+ All rights reserved.
+
+About the network method
+~~~~~~~~~~~~~~~~~~~~~~~
+
+The network procedure is described in detail in:
+1) Rost, Burkhard; Sander, Chris:
+ Prediction of protein structure at better than 70% accuracy.
+ J. Mol. Biol., 1993, 232, 584-599.
+
+A brief description is given in:
+ Rost, Burkhard; Sander, Chris:
+ Improved prediction of protein secondary structure by use of se-
+ quence profiles and neural networks.
+ Proc. Natl. Acad. Sci. U.S.A., 1993, 90, 7558-7562.
+
+The PHD mail server is described in:
+2) Rost, Burkhard; Sander, Chris; Schneider, Reinhard:
+ PHD - an automatic mail server for protein secondary structure
+ prediction.
+ CABIOS, 1994, 10, 53-60.
+
+The latest improvement steps (up to 72%) are explained in:
+3) Rost, Burkhard; Sander, Chris:
+ Combining evolutionary information and neural networks to predict
+ protein secondary structure.
+ Proteins, 1994, 19, 55-72.
+
+To be quoted for publications of PHD output:
+ Papers 1-3 for the prediction of secondary structure and the pre-
+ diction server.
+
+About the input to the network
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The prediction is performed by a system of neural networks.
+The input is a multiple sequence alignment. It is taken from an HSSP
+file (produced by the program MaxHom:
+ Sander, Chris & Schneider, Reinhard: Database of Homology-Derived
+ Structures and the Structural Meaning of Sequence Alignment.
+ Proteins, 1991, 9, 56-68.
+
+For optimal results the alignment should contain sequences with varying
+degrees of sequence similarity relative to the input protein.
+The following is an ideal situation:
+
++-----------------+----------------------+
+| sequence: | sequence identity |
++-----------------+----------------------+
+| target sequence | 100 % |
+| aligned seq. 1 | 90 % |
+| aligned seq. 2 | 80 % |
+| ... | ... |
+| aligned seq. 7 | 30 % |
++-----------------+----------------------+
+
+Estimated Accuracy of Prediction
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+A careful cross validation test on some 250 protein chains (in total
+about 55,000 residues) with less than 25% pairwise sequence identity
+gave the following results:
+
+++================++-----------------------------------------+
+|| Qtotal = 72.1% || ("overall three state accuracy") |
+++================++-----------------------------------------+
+
++----------------------------+-----------------------------+
+| Qhelix (% of observed)=70% | Qhelix (% of predicted)=77% |
+| Qstrand(% of observed)=62% | Qstrand(% of predicted)=64% |
+| Qloop (% of observed)=79% | Qloop (% of predicted)=72% |
++----------------------------+-----------------------------+
+..........................................................................
+
+These percentages are defined by:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+| number of correctly predicted residues
+|Qtotal = --------------------------------------- (*100)
+| number of all residues
+|
+| no of res correctly predicted to be in helix
+|Qhelix (% of obs) = -------------------------------------------- (*100)
+| no of all res observed to be in helix
+|
+|
+| no of res correctly predicted to be in helix
+|Qhelix (% of pred)= -------------------------------------------- (*100)
+| no of all residues predicted to be in helix
+
+..........................................................................
+
+Averaging over single chains
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The most reasonable way to compute the overall accuracies is the above
+quoted percentage of correctly predicted residues. However, since the
+user is mainly interested in the expected performance of the prediction
+for a particular protein, the mean value when averaging over protein
+chains might be of help as well. Computing first the three state
+accuracy for each protein chain, and then averaging over 250 chains
+yields the following average:
+
++-------------------------------====--+
+| Qtotal/averaged over chains = 72.2% |
++-------------------------------====--+
+| standard deviation = 9.3% |
++-------------------------------------+
+
+..........................................................................
+
+Further measures of performance
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Matthews correlation coefficient:
+
++---------------------------------------------+
+| Chelix = 0.63, Cstrand = 0.53, Cloop = 0.52 |
++---------------------------------------------+
+..........................................................................
+
+Average length of predicted secondary structure segments:
+
+. +------------+----------+
+. | predicted | observed |
++-----------+------------+----------+
+| Lhelix = | 10.3 | 9.3 |
+| Lstrand = | 5.0 | 5.3 |
+| Lloop = | 7.2 | 5.9 |
++-----------+------------+----------+
+..........................................................................
+
+The accuracy matrix in detail:
+
++---------------------------------------+
+| number of residues with H, E, L |
++---------+------+------+------+--------+
+| |net H |net E |net L |sum obs |
++---------+------+------+------+--------+
+| obs H |12447 | 1255 | 3990 | 17692 |
+| obs E | 949 | 7493 | 3750 | 12192 |
+| obs L | 2604 | 2875 |19962 | 25441 |
++---------+------+------+------+--------+
+| sum Net |16000 |11623 |27702 | 55325 |
++---------+------+------+------+--------+
+
+Note: This table is to be read in the following manner:
+ 12447 of all residues predicted to be in helix, were observed to
+ be in helix, 949 however belong to observed strands, 2604 to
+ observed loop regions. The term "observed" refers to the DSSP
+ assignment of secondary structure calculated from 3D coordinates
+ of experimentally determined structures (Dictionary of Secondary
+ Structure of Proteins: Kabsch & Sander (1983) Biopolymers, 22,
+ 2577-2637).
+
+Position-specific reliability index
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The network predicts the three secondary structure types using real
+numbers from the output units. The prediction is assigned by choosing
+the maximal unit ("winner takes all"). However, the real numbers
+contain additional information.
+E.g. the difference between the maximal and the second largest output
+unit can be used to derive a "reliability index". This index is given
+for each residue along with the prediction. The index is scaled to
+have values between 0 (lowest reliability), and 9 (highest).
+The accuracies (Qtot) to be expected for residues with values above a
+particular value of the index are given below as well as the fraction
+of such residues (%res).:
+
++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
+| index| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
+| %res |100.0| 99.2| 90.4| 80.9| 71.6| 62.5| 52.8| 42.3| 29.8| 14.1|
++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
+| | | | | | | | | | | |
+| Qtot | 72.1| 72.3| 74.8| 77.7| 80.3| 82.9| 85.7| 88.5| 91.1| 94.2|
+| | | | | | | | | | | |
++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
+| H%obs| 70.4| 70.6| 73.7| 77.1| 80.1| 83.1| 86.0| 89.3| 92.5| 96.4|
+| E%obs| 61.5| 61.7| 63.7| 66.6| 69.1| 71.7| 74.6| 77.0| 77.8| 68.1|
+| | | | | | | | | | | |
+| H%prd| 77.8| 78.0| 80.0| 82.6| 84.7| 86.9| 89.2| 91.3| 93.1| 95.4|
+| E%prd| 64.5| 64.7| 67.8| 71.0| 74.2| 77.6| 81.4| 85.1| 89.8| 93.5|
++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
+
+The above table gives the cumulative results, e.g. 62.5% of all
+residues have a reliability of at least 5. The overall three-state
+accuracy for this subset of almost two thirds of all residues is 82.9%.
+For this subset, e.g., 83.1% of the observed helices are correctly
+predicted, and 86.9% of all residues predicted to be in helix are
+correct.
+
+..........................................................................
+
+The following table gives the non-cumulative quantities, i.e. the
+values per reliability index range. These numbers answer the question:
+how reliable is the prediction for all residues labeled with the
+particular index i.
+
++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+
+| index| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
+| %res | 8.8| 9.5| 9.3| 9.1| 9.7| 10.5| 12.5| 15.7| 14.1|
++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+
+| | | | | | | | | | |
+| Qtot | 46.6| 50.6| 57.7| 62.6| 67.9| 74.2| 82.2| 88.3| 94.2|
+| | | | | | | | | | |
++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+
+| H%obs| 36.8| 42.3| 49.5| 55.2| 61.7| 69.9| 78.8| 87.4| 96.4|
+| E%obs| 44.7| 44.5| 52.1| 55.4| 60.9| 68.0| 75.9| 81.0| 68.1|
+| | | | | | | | | | |
+| H%prd| 49.9| 52.5| 60.3| 64.2| 69.2| 77.5| 85.4| 89.9| 95.4|
+| E%prd| 41.7| 47.1| 53.6| 57.0| 64.0| 71.6| 78.8| 88.8| 93.5|
++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+
+
+For example, for residues with Relindex = 5 64% of all predicted betha-
+strand residues are correctly identified.
+
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ Solvent accessibility prediction by PHDacc:
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+ Author: Burkhard Rost
+ EMBL, Heidelberg, FRG
+ Meyerhofstrasse 1, 69 117 Heidelberg
+ Internet: Rost@EMBL-Heidelberg.DE
+
+ All rights reserved.
+
+About the network method
+~~~~~~~~~~~~~~~~~~~~~~~
+
+The network for prediction of secondary structure is described in
+detail in:
+ Rost, Burkhard; Sander, Chris:
+ Prediction of protein structure at better than 70% accuracy.
+ J. Mol. Biol., 1993, 232, 584-599.
+
+The analysis of the prediction of solvent exposure is given in:
+ Rost, Burkhard; Sander, Chris:
+ Conservation and prediction of solvent accessibility in protein
+ families. Proteins, 1994, 20, 216-226.
+
+To be quoted for publications of PHD exposure prediction:
+ Both papers quoted above.
+
+Definition of accessibility
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+For training the residue solvent accessibility the DSSP (Dictionary of
+Secondary Structure of Proteins; Kabsch & Sander (1983) Biopolymers, 22,
+2577-2637) values of accessible surface area have been used. The
+prediction provides values for the relative solvent accessibility. The
+normalisation is the following:
+
+| ACCESSIBILITY (from DSSP in Angstrom)
+|RELATIVE_ACCESSIBILITY = ------------------------------------- * 100
+| MAXIMAL_ACC (amino acid type i)
+
+where MAXIMAL_ACC (i) is the maximal accessibility of amino acid type i.
+The maximal values are:
+
++----+----+----+----+----+----+----+----+----+----+----+----+
+| A | B | C | D | E | F | G | H | I | K | L | M |
+| 106| 160| 135| 163| 194| 197| 84| 184| 169| 205| 164| 188|
++----+----+----+----+----+----+----+----+----+----+----+----+
+| N | P | Q | R | S | T | V | W | X | Y | Z |
+| 157| 136| 198| 248| 130| 142| 142| 227| 180| 222| 196|
++----+----+----+----+----+----+----+----+----+----+----+
+
+Notation: one letter code for amino acid, B stands for D or N; Z stands
+ for E or Q; and X stands for undetermined.
+
+The relative solvent accessibility can be used to estimate the number
+of water molecules (W) in contact with the residue:
+
+W = ACCESSIBILITY /10
+
+The prediction is given in 10 states for relative accessibility, with
+
+RELATIVE_ACCESSIBILITY = (PREDICTED_ACC * PREDICTED_ACC)
+
+where PREDICTED_ACC = 0 - 9.
+
+Estimated Accuracy of Prediction
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+A careful cross validation test on some 238 protein chains (in total
+about 62,000 residues) with less than 25% pairwise sequence identity
+gave the following results:
+
+Correlation
+...........
+
+The correlation between observed and predicted solvent accessibility
+is:
+
+-----------
+corr = 0.53
+-----------
+
+This value ought to be compared to the worst and best case prediction
+scenario: random prediction (corr = 0.0) and homology modelling
+(corr = 0.66). (Note: homology modelling yields a relative accurate
+prediction in 3D if, and only if, a significantly identical sequence
+has a known 3D structure.)
+
+3-state accuracy
+................
+
+Often the relative accessibility is projected onto, e.g., 3 states:
+ b = buried (here defined as < 9% relative accessibility),
+ i = intermediate ( 9% <= rel. acc. < 36% ),
+ e = exposed ( rel. acc. >= 36% ).
+
+A projection onto 3 states or 2 states (buried/exposed) enables the
+compilation of a 3- and 2-state prediction accuracy. PHD reaches an
+overall 3-state accuracy of:
+ Q3 = 57.5%
+(compared to 35% for random prediction and 70% for homology modelling).
+
+In detail:
+
++-----------------------------------+-------------------------+
+| Qburied (% of observed)=77% | Qb (% of predicted)=60% |
+| Qintermediate (% of observed)= 9% | Qi (% of predicted)=44% |
+| Qexposed (% of observed)=78% | Qe (% of predicted)=56% |
++-----------------------------------+-------------------------+
+
+10-state accuracy
+.................
+
+The network predicts relative solvent accessibility in 10 states, with
+state i (i = 0-9) corresponding to a relative solvent accessibility of
+i*i %. The 10-state accuracy of the network is:
+
+ Q10 = 24.5%
+
+..........................................................................
+
+These percentages are defined by:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+| number of correctly predicted residues
+|Q3 = --------------------------------------- (*100)
+| number of all residues
+|
+| no of res. correctly predicted to be buried
+|Qburied (% of obs) = ------------------------------------------- (*100)
+| no of all res. observed to be buried
+|
+|
+| no of res. correctly predicted to be buried
+|Qburied (% of pred)= ------------------------------------------- (*100)
+| no of all residues predicted to be buried
+
+..........................................................................
+
+Averaging over single chains
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The most reasonable way to compute the overall accuracies is the above
+quoted percentage of correctly predicted residues. However, since the
+user is mainly interested in the expected performance of the prediction
+for a particular protein, the mean value when averaging over protein
+chains might be of help as well. Computing first the correlation
+between observed and predicted accessibility for each protein chan, and
+then averaging over all 238 chains yields the following average:
+
++-------------------------------====--+
+| corr/averaged over chains = 0.53 |
++-------------------------------====--+
+| standard deviation = 0.11 |
++-------------------------------------+
+
+..........................................................................
+
+Further details of performance accuracy
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The accuracy matrix in detail:
+..............................
+
+-------+----------------------------------------------------+-----------
+\ PHD | 0 1 2 3 4 5 6 7 8 9 | SUM %obs
+-------+----------------------------------------------------+-----------
+OBS 0 | 8611 140 8 44 82 169 772 334 27 0 | 10187 16.6
+OBS 1 | 4367 164 0 50 106 231 738 346 44 3 | 6049 9.8
+OBS 2 | 3194 168 1 68 125 303 951 513 42 7 | 5372 8.7
+OBS 3 | 2760 159 8 80 136 327 1246 746 58 19 | 5539 9.0
+OBS 4 | 2312 144 2 72 166 396 1615 1245 124 19 | 6095 9.9
+OBS 5 | 1873 96 3 84 138 425 1979 1834 187 27 | 6646 10.8
+OBS 6 | 1387 67 1 60 80 278 2237 2627 231 51 | 7019 11.4
+OBS 7 | 1082 35 0 32 56 225 1871 3107 302 60 | 6770 11.0
+OBS 8 | 660 25 0 27 43 136 1206 2374 325 87 | 4883 7.9
+OBS 9 | 325 20 2 27 29 74 648 1159 366 214 | 2864 4.7
+-------+----------------------------------------------------+-----------
+SUM |26571 1018 25 544 961 2564 13263 14285 1706 487 |
+-------+----------------------------------------------------+-----------
+
+Note: This table is to be read in the following manner:
+ 8611 of all residues predicted to be in exposed by 0%, were
+ observed with 0% relative accessibility. However, 325 of all
+ residues predicted to have 0% are observed as completely exposed
+ (obs = 9 -> rel. acc. >= 81%). The term "observed" refers to the
+ DSSP compilation of area of solvent accessibility calculated from
+ 3D coordinates of experimentally determined structures (Diction-
+ ary of Secondary Structure of Proteins: Kabsch & Sander (1983)
+ Biopolymers, 22, 2577-2637).
+
+Accuracy for each amino acid:
+.............................
+
++---+------------------------------+-----+-------+------+
+|AA | Q3 b%o b%p i%o i%p e%o e%p | Q10 | corr | N |
++---+------------------------------+-----+-------+------+
+| A | 59.0 87 60 2 38 66 57 | 31 | 0.530 | 5054 |
+| C | 62.0 91 67 5 39 25 21 | 34 | 0.244 | 893 |
+| D | 56.5 21 45 6 49 94 57 | 20 | 0.321 | 3536 |
+| E | 60.8 9 40 3 41 98 61 | 21 | 0.347 | 3743 |
+| F | 63.3 94 67 9 46 29 37 | 27 | 0.366 | 2436 |
+| G | 52.1 75 51 1 31 67 53 | 22 | 0.405 | 4787 |
+| H | 50.9 63 53 23 45 71 50 | 18 | 0.442 | 1366 |
+| I | 64.9 95 68 6 41 30 38 | 34 | 0.360 | 3437 |
+| K | 66.6 2 11 2 37 98 67 | 23 | 0.267 | 3652 |
+| L | 61.6 93 65 8 44 31 40 | 31 | 0.368 | 5016 |
+| M | 60.1 92 64 5 39 45 44 | 29 | 0.452 | 1371 |
+| N | 55.5 45 45 8 38 87 59 | 17 | 0.410 | 2923 |
+| P | 53.0 48 48 9 39 83 56 | 18 | 0.364 | 2920 |
+| Q | 54.3 27 44 7 44 92 56 | 20 | 0.344 | 2225 |
+| R | 49.9 15 47 36 47 76 51 | 18 | 0.372 | 2765 |
+| S | 55.6 69 53 3 51 81 56 | 22 | 0.464 | 3981 |
+| T | 51.8 61 51 8 38 78 53 | 21 | 0.432 | 3740 |
+| V | 61.1 93 65 5 40 39 42 | 34 | 0.418 | 4156 |
+| W | 56.2 85 62 20 49 29 27 | 21 | 0.318 | 891 |
+| Y | 49.7 73 52 33 49 36 38 | 19 | 0.359 | 2301 |
++---+------------------------------+-----+-------+------+
+
+Abbreviations:
+
+AA: amino acid in one-letter code
+b%o, i%o, e%o: = Qburied, Qintermediate, Qexposed (% of observed),
+ i.e. percentage of correct prediction in each state, see above
+b%p, i%p, e%p: = Qburied, Qintermediate, Qexposed (% of predicted),
+ i.e. probability of correct prediction in each state, see above
+b%o: = Qburied (% of observed), see above
+Q10: percentage of correctly predicted residues in each of the 10
+ states of predicted relative accessibility.
+corr: correlation between predicted and observed rel. acc.
+N: number of residues in data set
+
+Accuracy for different secondary structure:
+...........................................
+
++--------+------------------------------+----+-------+-------+
+| type | Q3 b%o b%p i%o i%p e%o e%p |Q10 | corr | N |
++--------+------------------------------+----+-------+-------+
+| helix | 59.5 79 64 8 44 80 56 | 27 | 0.574 | 20100 |
+| strand | 61.3 84 73 9 46 69 37 | 35 | 0.524 | 13356 |
+| loop | 54.4 64 43 11 44 78 61 | 18 | 0.442 | 27968 |
++--------+------------------------------+----+-------+-------+
+
+Abbreviations as before.
+
+Position-specific reliability index
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The network predicts the 10 states for relative accessibility using real
+numbers from the output units. The prediction is assigned by choosing
+the maximal unit ("winner takes all"). However, the real numbers
+contain additional information.
+E.g. the difference between the maximal and the second largest output
+unit (with the constraint that the second largest output is compiled
+among all units at least 2 positions off the maximal unit) can be used
+to derive a "reliability index". This index is given for each residue
+along with the prediction. The index is scaled to have values between
+0 (lowest reliability), and 9 (highest).
+The accuracies (Q3, corr, asf.) to be expected for residues with values
+above a particular value of the index are given below as well as the
+fraction of such residues (%res).:
+
++---+------------------------------+----+-------+-------+
+|RI | Q3 b%o b%p i%o i%p e%o e%p |Q10 | corr | %res |
++---+------------------------------+----+-------+-------+
+| 0 | 57.5 77 60 9 44 78 56 | 24 | 0.535 | 100.0 |
+| 1 | 59.1 76 63 9 45 82 57 | 25 | 0.560 | 91.2 |
+| 2 | 61.7 79 66 4 47 87 58 | 27 | 0.594 | 77.1 |
+| 3 | 66.6 87 70 1 51 89 63 | 30 | 0.650 | 57.1 |
+| 4 | 70.0 89 72 0 83 91 67 | 32 | 0.686 | 45.8 |
+| 5 | 72.9 92 75 0 0 93 70 | 34 | 0.722 | 35.6 |
+| 6 | 76.3 95 77 0 0 93 75 | 36 | 0.769 | 24.7 |
+| 7 | 79.0 97 79 0 0 93 78 | 39 | 0.803 | 16.0 |
+| 8 | 80.9 98 80 0 0 91 81 | 43 | 0.824 | 9.6 |
+| 9 | 81.2 99 80 0 0 88 83 | 45 | 0.828 | 5.9 |
++---+------------------------------+----+-------+-------+
+
+Abbreviations as before.
+
+The above table gives the cumulative results, e.g. 45.8% of all
+residues have a reliability of at least 4. The correlation for this
+most reliably predicted half of the residues is 0.686, i.e. a value
+comparable to what could be expected if homology modelling were
+possible. For this subset of 45.8% of all residues, 89% of the buried
+residues are correctly predicted, and 72% of all residues predicted to
+be buried are correct.
+
+..........................................................................
+
+The following table gives the non-cumulative quantities, i.e. the
+values per reliability index range. These numbers answer the question:
+how reliable is the prediction for all residues labeled with the
+particular index i.
+
++---+------------------------------+----+-------+-------+
+|RI | Q3 b%o b%p i%o i%p e%o e%p |Q10 | corr | %res |
++---+------------------------------+----+-------+-------+
+| 0 | 40.9 79 40 16 41 21 40 | 14 | 0.175 | 8.8 |
+| 1 | 45.4 61 46 28 44 48 44 | 17 | 0.278 | 14.1 |
+| 2 | 47.4 53 52 10 46 80 44 | 19 | 0.343 | 19.9 |
+| 3 | 52.9 75 59 4 50 77 47 | 23 | 0.439 | 11.4 |
+| 4 | 60.0 81 63 0 83 84 56 | 25 | 0.547 | 10.1 |
+| 5 | 65.2 82 70 0 0 93 62 | 28 | 0.607 | 10.9 |
+| 6 | 71.3 90 72 0 0 94 70 | 31 | 0.692 | 8.8 |
+| 7 | 76.0 94 76 0 0 95 75 | 34 | 0.762 | 6.3 |
+| 8 | 80.5 97 81 0 0 94 79 | 39 | 0.808 | 3.8 |
+| 9 | 81.2 99 80 0 0 88 83 | 45 | 0.828 | 5.9 |
++---+------------------------------+----+-------+-------+
+
+For example, for residues with RI = 4 83% of all predicted intermediate
+residues are correctly predicted as such.
+
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ Prediction of helical transmembrane segments by PHDhtm:
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+ Author: Burkhard Rost
+ EMBL, Heidelberg, FRG
+ Meyerhofstrasse 1, 69 117 Heidelberg
+ Internet: Rost@EMBL-Heidelberg.DE
+
+ All rights reserved.
+
+About the network method
+~~~~~~~~~~~~~~~~~~~~~~~
+
+The PHD mail server is described in:
+ Rost, Burkhard; Sander, Chris; Schneider, Reinhard:
+ PHD - an automatic mail server for protein secondary structure
+ prediction.
+ CABIOS, 1994, 10, 53-60.
+
+To be quoted for publications of PHDhtm output:
+ Rost, Burkhard; Casadio, Rita; Fariselli, Piero; Sander, Chris:
+ Prediction of helical transmembrane segments at 95% accuracy.
+ Protein Science, 1995, 4, 521-533.
+
+Estimated Accuracy of Prediction
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+A cross validation test on 69 helical trans-membrane proteins (in total
+about 30,000 residues) with less than 25% pairwise sequence identity
+gave the following results:
+
+++================++-----------------------------------------+
+|| Qtotal = 94.7% || ("overall two state accuracy") |
+++================++-----------------------------------------+
+
++----------------------------+-----------------------------+
+| Qhelix (% of observed)=92% | Qhelix (% of predicted)=83% |
+| Qloop (% of observed)=96% | Qloop (% of predicted)=97% |
++----------------------------+-----------------------------+
+
+..........................................................................
+
+These percentages are defined by:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+| number of correctly predicted residues
+|Qtotal = --------------------------------------- (*100)
+| number of all residues
+|
+| no of res correctly predicted to be in helix
+|Qhelix (% of obs) = -------------------------------------------- (*100)
+| no of all res observed to be in helix
+|
+|
+| no of res correctly predicted to be in helix
+|Qhelix (% of pred)= -------------------------------------------- (*100)
+| no of all residues predicted to be in helix
+
+..........................................................................
+
+Further measures of performance
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Matthews correlation coefficient:
+
++---------------------------------------------+
+| Chelix = 0.84, Cloop = 0.84 |
++---------------------------------------------+
+..........................................................................
+
+Average length of predicted secondary structure segments:
+
+| +------------+----------+
+| | predicted | observed |
++-----------+------------+----------+
+| Lhelix = | 24.6 | 22.2 |
++-----------+------------+----------+
+..........................................................................
+
+The accuracy matrix in detail:
+
++---------------------------------+
+| number of residues with H, L |
++---------+------+-------+--------+
+| |net H | net L |sum obs |
++---------+------+-------+--------+
+| obs H | 5214 | 492 | 5706 |
+| obs L | 1050 | 22423 | 23473 |
++---------+------+-------+--------+
+| sum Net | 6264 | 22915 | 29179 |
++---------+------+-------+--------+
+
+Note: This table is to be read in the following manner:
+ 5214 of all residues predicted to be in a helical trans-membrane
+ region, were observed to be in the lipid bilayer, 1050 however
+ were observed either inside or outside of the protein, i.e. in
+ loop (or non-membrane) regions. The term "observed" refers to DSSP
+ assignment of secondary structure calculated from 3D coordinates
+ of experimentally determined structures (Dictionary of Secondary
+ Structure of Proteins: Kabsch & Sander (1983) Biopolymers, 22,
+ 2577-2637) where these were available. For all other proteins,
+ the assignment of trans-membrane segments has been taken from the
+ Swissprot data bank (Bairoch, A.; Boeckmann, B.: The SWISS-PROT
+ protein sequence data bank. Nucl. Acids Res. 20: 2019-2022, 1992).
+
+..........................................................................
+
+Overlap between predicted and observed segments:
+
++-----------------+---------------+----------------+
+| segment overlap | % of observed | % of predicted |
+| Sov helix | 95.6% | 95.5% |
+| Sov loop | 83.6% | 97.2% |
++-----------------+---------------+----------------+
+| Sov total | 86.0% | 96.8% |
++-----------------+---------------+----------------+
+
+ Definition of Sov in: Rost et al., JMB, 1994, 235, 13-26.
+
+ As helical trans-membrane segments are longer than globular heli-
+ ces, correctly predicted segments can easily be made out. PHDhtm
+ misses 5 out of 258 observed segments, predicts 6 where non is
+ observed and 3 times the predicted helical segment overlaps two
+ observed regions. Thus, in total more than 95% of all segments
+ are correctly predicted.
+
+..........................................................................
+
+Entropy of prediction (information measure):
+
++-----------------+
+| I = 0.64 |
++-----------------+
+
+ (For comparison: homology modelling of globular proteins in three
+ states: I=0.62.)
+ Definition of Sov in: Rost et al., JMB, 1994, 235, 13-26.
+
+Position-specific reliability index
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The network predicts two states: helical trans-membrane region and rest
+using two output units. The prediction is assigned by choosing the ma-
+ximal unit ("winner takes all"). However, the real numbers of the out-
+put units contain additional information.
+E.g. the difference between the two output units can be used to derive
+a "reliability index". This index is given for each residue along with
+the prediction. The index is scaled to have values between 0 (lowest
+reliability), and 9 (highest).
+The accuracies (Qtot) to be expected for residues with values above a
+particular value of the index are given below as well as the fraction
+of such residues (%res).:
+
++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
+| index| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
+| %res |100.0| 98.8| 97.3| 95.9| 94.1| 92.3| 89.9| 86.2| 75.0| 66.8|
++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
+| | | | | | | | | | | |
+| Qtot | 94.7| 95.2| 95.6| 96.2| 96.7| 97.2| 97.7| 98.4| 99.4| 99.8|
+| | | | | | | | | | | |
++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
+| H%obs| 91.8| 92.9| 93.8| 94.4| 95.0| 95.7| 96.2| 96.8| 95.5| 78.7|
+| L%obs| 95.3| 95.7| 96.1| 96.6| 97.0| 97.5| 98.1| 98.8| 99.7|100.0|
+| | | | | | | | | | | |
+| H%prd| 82.7| 83.8| 85.0| 86.7| 88.1| 89.7| 91.4| 93.8| 96.3| 97.1|
+| L%prd| 97.9| 98.3| 98.5| 98.7| 98.8| 99.0| 99.2| 99.4| 99.7| 99.9|
++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
+
+The above table gives the cumulative results, e.g. 92.3% of all
+residues have a reliability of at least 5. The overall two-state
+accuracy for this subset is 97.2%. For this subset, e.g., 95.7% of
+the observed helical trans-membrane residues are correctly predicted,
+and 89.7% of all residues predicted to be in helical trans-membrane
+segment are correct.
+
+The resulting network (PHD) prediction is:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+________________________________________________________________________________
+
+ PHD: Profile fed neural network systems from HeiDelberg
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+ Prediction of:
+ secondary structure, by PHDsec
+ solvent accessibility, by PHDacc
+ and helical transmembrane regions, by PHDhtm
+
+ Author:
+ Burkhard Rost
+ EMBL, 69012 Heidelberg, Germany
+ Internet: Rost@EMBL-Heidelberg.DE
+
+ All rights reserved.
+
+ The network systems are described in:
+
+ PHDsec: B Rost & C Sander: JMB, 1993, 232, 584-599.
+ B Rost & C Sander: Proteins, 1994, 19, 55-72.
+ PHDacc: B Rost & C Sander: Proteins, 1994, 20, 216-226.
+ PHDhtm: B Rost et al.: Prot. Science, 1995, 4, 521-533.
+
+ Some statistics
+ ~~~~~~~~~~~~~~~
+
+ Percentage of amino acids:
+ +--------------+--------+--------+--------+--------+--------+
+ | AA: | L | A | S | G | I |
+ | % of AA: | 13.0 | 10.0 | 9.7 | 8.9 | 8.6 |
+ +--------------+--------+--------+--------+--------+--------+
+ | AA: | V | R | T | F | D |
+ | % of AA: | 7.8 | 5.2 | 4.5 | 4.5 | 4.5 |
+ +--------------+--------+--------+--------+--------+--------+
+ | AA: | N | Q | E | P | K |
+ | % of AA: | 4.1 | 3.0 | 3.0 | 2.6 | 2.6 |
+ +--------------+--------+--------+--------+--------+--------+
+ | AA: | Y | M | W | H | C |
+ | % of AA: | 1.9 | 1.9 | 1.5 | 1.5 | 1.5 |
+ +--------------+--------+--------+--------+--------+--------+
+
+ Percentage of secondary structure predicted:
+ +--------------+--------+--------+--------+
+ | SecStr: | H | E | L |
+ | % Predicted: | 43.9 | 16.7 | 39.4 |
+ +--------------+--------+--------+--------+
+
+ According to the following classes:
+ all-alpha: %H>45 and %E< 5; all-beta : %H<5 and %E>45
+ alpha-beta : %H>30 and %E>20; mixed: rest,
+ this means that the predicted class is: mixed class
+
+ PHD output for your protein
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+ Tue Nov 24 17:44:57 1998
+ Jury on: 10 different architectures (version 5.94_317 ).
+ Note: differently trained architectures, i.e., different versions can
+ result in different predictions.
+
+ About the protein
+ ~~~~~~~~~~~~~~~~~
+
+ HEADER /home/phd/server/work/predict_h25873-220
+ COMPND
+ SOURCE
+ AUTHOR
+ SEQLENGTH 269
+ NCHAIN 1 chain(s) in predict_h25873-22040 data set
+ NALIGN 48
+ (=number of aligned sequences in HSSP file)
+
+ Abbreviations: PHDsec
+ ~~~~~~~~~~~~~~~~~~~~~
+
+ sequence:
+ AA : amino acid sequence
+ secondary structure:
+ HEL: H=helix, E=extended (sheet), blank=other (loop)
+ PHD: Profile network prediction HeiDelberg
+ Rel: Reliability index of prediction (0-9)
+ detail:
+ prH: 'probability' for assigning helix
+ prE: 'probability' for assigning strand
+ prL: 'probability' for assigning loop
+ note: the 'probabilites' are scaled to the interval 0-9, e.g.,
+ prH=5 means, that the first output node is 0.5-0.6
+ subset:
+ SUB: a subset of the prediction, for all residues with an expected
+ average accuracy > 82% (tables in header)
+ note: for this subset the following symbols are used:
+ L: is loop (for which above " " is used)
+ ".": means that no prediction is made for this residue, as the
+ reliability is: Rel < 5
+
+ Abbreviations: PHDacc
+ ~~~~~~~~~~~~~~~~~~~~~
+
+ SS : secondary structure
+ HEL: H=helix, E=extended (sheet), blank=other (loop)
+ solvent accessibility:
+ 3st: relative solvent accessibility (acc) in 3 states:
+ b = 0-9%, i = 9-36%, e = 36-100%.
+ PHD: Profile network prediction HeiDelberg
+ Rel: Reliability index of prediction (0-9)
+ O_3: observed relative acc. in 3 states: B, I, E
+ note: for convenience a blank is used intermediate (i).
+ P_3: predicted relative accessibility in 3 states
+ 10st:relative accessibility in 10 states:
+ = n corresponds to a relative acc. of n*n %
+ subset:
+ SUB: a subset of the prediction, for all residues with an expected
+ average correlation > 0.69 (tables in header)
+ note: for this subset the following symbols are used:
+ "I": is intermediate (for which above " " is used)
+ ".": means that no prediction is made for this residue, as the
+ reliability is: Rel < 4
+
+ Abbreviations: PHDhtm
+ ~~~~~~~~~~~~~~~~~~~~~
+
+ secondary structure:
+ HL: T=helical transmembrane region, blank=other (loop)
+ PHD: Profile network prediction HeiDelberg
+ PHDF:filtered prediction, i.e., too long transmembrane segments
+ are split, too short ones are deleted
+ Rel: Reliability index of prediction (0-9)
+ detail:
+ prH: 'probability' for assigning helical transmembrane region
+ prL: 'probability' for assigning loop
+ note: the 'probabilites' are scaled to the interval 0-9, e.g.,
+ prH=5 means, that the first output node is 0.5-0.6
+ subset:
+ SUB: a subset of the prediction, for all residues with an expected
+ average accuracy > 82% (tables in header)
+ note: for this subset the following symbols are used:
+ L: is loop (for which above " " is used)
+ ".": means that no prediction is made for this residue, as the
+ reliability is: Rel < 5
+
+ protein: predict length 269
+
+ ....,....1....,....2....,....3....,....4....,....5....,....6
+ AA |MASEIKKKLFWRAVVAEFLAMTLFVFISIGSALGFNYPLERNQTLVQDNVKVSLAFGLSI|
+ PHD sec | HHHHHHHHHHHHHHHHHHHHHHHHHHEE HHHHHHHHHHHHH|
+ Rel sec |998443148899999999999998997676530312469989998623353579999999|
+ detail:
+ prH sec |001223468899999999999998888777653112210000000145566788999999|
+ prE sec |000011000000000000000001001111233542100000000000323211000000|
+ prL sec |998665420100000000000000000011112244578988998753100000000000|
+ subset: SUB sec |LLL.....HHHHHHHHHHHHHHHHHHHHHHH......LLLLLLLLL...H.HHHHHHHHH|
+
+ ACCESSIBILITY
+ 3st: P_3 acc |eeeebee bbb bbbbbbbbbbbbbbbbbbbbbebeee eeeeeeeeebbbbbbbbbbbb|
+ 10st: PHD acc |997706650005000000000000000000000607775779776677000000000000|
+ Rel acc |735421110541467608662789996343122133420454330023453975664547|
+ subset: SUB acc |e.ee.....bb.bbbb.bbb.bbbbbb.b.......e..eee......bb.bbbbbbbbb|
+ ....,....7....,....8....,....9....,....10...,....11...,....12
+ AA |ATLAQSVGHISGAHSNPAVTLGLLLSCQISILRAVMYIIAQCVGAIVASAILSGITSSLL|
+ PHD sec |HHHHHHHHHE HHHHEHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH |
+ Rel sec |999996412122653167703135552356779999999999999999999998467213|
+ detail:
+ prH sec |998986544334223477843456665567779999999999999999999998611343|
+ prE sec |001001123420010000145432101221110000000000000000000000000000|
+ prL sec |000001232245765521000000123210000000000000000000000000278555|
+ subset: SUB sec |HHHHHH......LL..HHH....HHH..HHHHHHHHHHHHHHHHHHHHHHHHHH.LL...|
+
+ ACCESSIBILITY
+ 3st: P_3 acc |bbbbebbbebbbbbb bbbbbbbbbbbebbbbbbbbbbbbbbbbbbbbbbbbeebbeeeb|
+ 10st: PHD acc |000060006000000500000000000600000000000000000000000067006760|
+ Rel acc |456515321655013144869663400154551757478936465465467713401400|
+ subset: SUB acc |bbbb.b...bbb....bbbbbbb.b...bbbb.bbbbbbb.bbbbbbbbbbb..b..e..|
+ ....,....13...,....14...,....15...,....16...,....17...,....18
+ AA |ENSLGRNDLARGVNSGQGLGIEIIGTLQLVLCVLATTDRRRRDLGGSAPLAIGLSVALGH|
+ PHD sec | HHH EEEEEEEEEEEEEEEEEEE E E HHHHHH|
+ Rel sec |359985212134223651899898866789799875436658889963211351457756|
+ detail:
+ prH sec |320002345432332111000000000000100000221120000000001113567767|
+ prE sec |100000000000011014899888877789789886100000000013544222221111|
+ prL sec |568986543466545763100000011100000112567768889975454564210111|
+ subset: SUB sec |.LLLLL.........LL.EEEEEEEEEEEEEEEEEE..LLLLLLLLL.....L..HHHHH|
+
+ ACCESSIBILITY
+ 3st: P_3 acc |eeebbbebbbeebeebeebbbbbbbbbbbbbbbbbbbeeeeeeeebbbbbbbbbbbbbbb|
+ 10st: PHD acc |677000600077076077000000000000000000077767767000000000000000|
+ Rel acc |133100124043040233247198656399879530035414413123255869586654|
+ subset: SUB acc |........b.e..e.....bb.bbbbb.bbbbbb....ee.ee......bbbbbbbbbbb|
+ ....,....19...,....20...,....21...,....22...,....23...,....24
+ AA |LLAIDYTGCGINPARSFGSAVLTRNFSNHWIFWVGPFIGSALAVLIYDFILAPRSSDFTD|
+ PHD sec |HEEEE E HHHEEEE EEEEEE HHHHHHHHHHHHHEEEEE |
+ Rel sec |321341126989622145152653534229996251699999999973147525556642|
+ detail:
+ prH sec |521100000000145432463121122000000114789999999875421111121124|
+ prE sec |244564431000000000015765121358997510000000000013467642110000|
+ prL sec |233234457889754567411012655530002364200000000010010136667765|
+ subset: SUB sec |........LLLLL....H.H.EE.L....EEEE.L.HHHHHHHHHHH...EE.LLLLL..|
+
+ ACCESSIBILITY
+ 3st: P_3 acc |bbbbebbbbbbebb bbbbbbbbeebeebbbbbbbbbbbbbbbbbbbbbbbbeeeee ee|
+ 10st: PHD acc |000060000006005000000007606600000000000000000000000076777577|
+ Rel acc |754424240102242141047612131118967874356346635751777031345044|
+ subset: SUB acc |bbbb.b.b.....b..b..bbb.......bbbbbbb.bb.bbb.bbb.bbb....ee.ee|
+ ....,....25...,....26...,....27...,....28...,....29...,....30
+ AA |RMKVWTSGQVEEYDLDADDINSRVEMKPK|
+ PHD sec |HHHHHH |
+ Rel sec |66775259975467555457776422699|
+ detail:
+ prH sec |77887520012221222221111100000|
+ prE sec |00000000000000000000001233200|
+ prL sec |11112379987678777678887655799|
+ subset: SUB sec |HHHHH.LLLLL.LLLLL.LLLLL...LLL|
+
+ ACCESSIBILITY
+ 3st: P_3 acc |ebebbeeeeeeeeeeeeeeeeeebeeeee|
+ 10st: PHD acc |60700787677777677777767067789|
+ Rel acc |10411563134335144444514212559|
+ subset: SUB acc |..e..ee...e..e.eeeeee.e...eee|
+
+ PHDhtm Helical transmembrane prediction
+ note: PHDacc and PHDsec are reliable for water-
+ soluble globular proteins, only. Thus,
+ please take the predictions above with
+ particular caution wherever transmembrane
+ helices are predicted by PHDhtm!
+
+ PHDhtm
+---
+--- PhdTopology REFINEMENT AND TOPOLOGY PREDICTION: SYMBOLS
+--- AA : amino acid in one-letter code
+--- PHD htm : HTM's predicted by the PHD neural network
+--- system (T=HTM, ' '=not HTM)
+--- Rel htm : Reliability index of prediction (0-9, 0 is low)
+--- detail : Neural network output in detail
+--- prH htm : 'Probability' for assigning a helical trans-
+--- membrane region (HTM)
+--- prL htm : 'Probability' for assigning a non-HTM region
+--- note: 'Probabilites' are scaled to the interval
+--- 0-9, e.g., prH=5 means, that the first
+--- output node is 0.5-0.6
+--- subset : Subset of more reliable predictions
+--- SUB htm : All residues for which the expected average
+--- accuracy is > 82% (tables in header).
+--- note: for this subset the following symbols are used:
+--- L: is loop (for which above ' ' is used)
+--- '.': means that no prediction is made for this,
+--- residue as the reliability is: Rel < 5
+--- other : predictions derived based on PHDhtm
+--- PHDFhtm : filtered prediction, i.e., too long HTM's are
+--- split, too short ones are deleted
+--- PHDRhtm : refinement of neural network output
+--- PHDThtm : topology prediction based on refined model
+--- symbols used:
+--- i: intra-cytoplasmic
+--- T: transmembrane region
+--- o: extra-cytoplasmic
+---
+--- PhdTopology REFINEMENT AND TOPOLOGY PREDICTION
+ ....,....1....,....2....,....3....,....4....,....5....,....6
+ AA |MASEIKKKLFWRAVVAEFLAMTLFVFISIGSALGFNYPLERNQTLVQDNVKVSLAFGLSI|
+ PHD htm | TTTTTTTTTTTTTTTTTTT TTTTTTTTTTTT|
+ detail: | |
+ prH htm |000000000001136788999999999988875321110000000123678889999988|
+ prL htm |999999999998863211000000000011124678889999999876321110000011|
+ other: | |
+ PHDFhtm | TTTTTTTTTTTTTTTTTTT TTTTTTTTTTT|
+ PHDRhtm | TTTTTTTTTTTTTTTTTT TTTTTTTTTTT|
+ PHDThtm |iiiiiiiiiiiiiiTTTTTTTTTTTTTTTTTToooooooooooooooooTTTTTTTTTTT|
+ subset: | |
+ SUB htm |............................................................|
+ ....,....7....,....8....,....9....,....10...,....11...,....12
+ AA |ATLAQSVGHISGAHSNPAVTLGLLLSCQISILRAVMYIIAQCVGAIVASAILSGITSSLL|
+ PHD htm |TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT |
+ detail: | |
+ prH htm |888888877777666677788888888888888888888888888888888876543211|
+ prL htm |111111122222333322211111111111111111111111111111111123456788|
+ other: | |
+ PHDFhtm |TTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT |
+ PHDRhtm |TTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTT |
+ PHDThtm |TTTTTTTTiiiiiiiiiiiiiTTTTTTTTTTTTTTTTTTTTTTTTToooooooooooooo|
+ subset: | |
+ SUB htm |............................................................|
+ ....,....13...,....14...,....15...,....16...,....17...,....18
+ AA |ENSLGRNDLARGVNSGQGLGIEIIGTLQLVLCVLATTDRRRRDLGGSAPLAIGLSVALGH|
+ PHD htm | TTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTT|
+ detail: | |
+ prH htm |000000000001234567788888999988887643211111111235788899998888|
+ prL htm |999999999998765432211111000011112356788888888764211100001111|
+ other: | |
+ PHDFhtm | TTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTT|
+ PHDRhtm | TTTTTTTTTTTTTTTTTT TTTTTTTTTTTT|
+ PHDThtm |ooooooooooooooooTTTTTTTTTTTTTTTTTTiiiiiiiiiiiiiiTTTTTTTTTTTT|
+ subset: | |
+ SUB htm |............................................................|
+ ....,....19...,....20...,....21...,....22...,....23...,....24
+ AA |LLAIDYTGCGINPARSFGSAVLTRNFSNHWIFWVGPFIGSALAVLIYDFILAPRSSDFTD|
+ PHD htm |TTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT |
+ detail: | |
+ prH htm |888887765443432233334566777777788888888888888888887542100000|
+ prL htm |111112234556567766665433222222211111111111111111112457899999|
+ other: | |
+ PHDFhtm |TTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT |
+ PHDRhtm |TTTTTT TTTTTTTTTTTTTTTTTTT |
+ PHDThtm |TTTTTToooooooooooooooooooooooooTTTTTTTTTTTTTTTTTTTiiiiiiiiii|
+ subset: | |
+ SUB htm |............................................................|
+ ....,....25...,....26...,....27...,....28...,....29...,....30
+ AA |RMKVWTSGQVEEYDLDADDINSRVEMKPK|
+ PHD htm | |
+ detail: | |
+ prH htm |00000000000000000000000000000|
+ prL htm |99999999999999999999999999999|
+ other: | |
+ PHDFhtm | |
+ PHDRhtm | |
+ PHDThtm |iiiiiiiiiiiiiiiiiiiiiiiiiiiii|
+ subset: | |
+ SUB htm |.............................|
+---
+--- PhdTopology REFINEMENT AND TOPOLOGY PREDICTION END
+---
+
+________________________________________________________________________________
+
+________________________________________________________________________________
+
+-----------------------------------------------------------------------------
+--- PredictProtein: NEWS from January, 1997 ---
+--- ---
+--- Dear user, ---
+--- ---
+--- as of January 1, 1997, EMBL has effectively decided to not ---
+--- support the PredictProtein service by personal resources. I do ---
+--- maintain the program, so to speak, in my private time. However, ---
+--- my contract obliges me to do science, instead. Unfortunately, ---
+--- the computer environment at EMBL is at the same time starting ---
+--- to become increasingly unstable. Consequence of these two re- ---
+--- cent developments is that the PredictProtein service is not as ---
+--- stable as it was. ---
+--- ---
+--- I apologise for the problems this may cause. In particular, ---
+--- I apologise for my inability to reply to the 20-30 daily, per- ---
+--- sonal mails, and suggest to re-submit requests after 24 hours! ---
+--- ---
+--- Hoping that I shall find a more convenient solution for the ---
+--- future of the PredictProtein I remain with my best regards, ---
+--- ---
+--- Burkhard Rost ---
+-----------------------------------------------------------------------------
+--- PredictProtein: NEWS from April, 1998 ---
+--- ---
+-------------------------------- ---
+--- MOVING PredictProtein ---
+--- There appears to be light on the horizon! PP will may be having ---
+--- many hickups over the next months (as I shall leave EMBL). How- ---
+--- ever, the server seems to have a fair chance of survival thanks ---
+--- to a major support that is being raised by Columbia University, ---
+--- New York, U.S.A.). I hope that this will settle the issue for ---
+--- the years to come ... ---
+-------------------------------- ---
+--- WARNING ---
+--- After a major rewriting of most of the PP code over the last, ---
+--- I am afraid that not all errors have been traced by me, yet. ---
+--- Thus, please have mercy and report any bug you'll encounter! ---
+--- THANKS, Burkhard Rost ---
+-------------------------------- ---
+--- NEW PREDICTION DEFAULTS ---
+--- * Coiled-coil regions: now by default the program COILS written by ---
+--- Andrei Lupas is run on your sequence. An output is returned if a ---
+--- coiled-coil region has been detected. ---
+--- * Functional sequence motifs: now by default the PROSITE database ---
+--- written by Amos Bairoch, Philip Bucher and Kay Hofmann is scanned ---
+--- for sequence motifs. An output is returned if any motif has been ---
+--- detected. ---
+-------------------------------- ---
+--- see http://www.embl-heidelberg.de/predictprotein/ppNews.html ---
+--- for a description of the following new options. ---
+--- NEW INPUT OPTION ---
+--- * Your input sequence(s) in FASTA-list format ("# FASTA list ") ---
+--- NEW OUTPUT OPTIONS ---
+--- * Return also BLASTP output ("return blast") ---
+--- * Return prediction additionally in RDB format ("return phd rdb") ---
+--- * Return topits hssp ("return topits hssp") ---
+--- * Return topits strip ("return topits strip") ---
+--- * Return topits own ("return topits own") ---
+--- * Return no coils ("return no coils") ---
+--- * Return no prosite ("return no prosite") ---
+-----------------------------------------------------------------------------
diff --git a/Master/texmf-dist/doc/latex/texshade/AQP1.top b/Master/texmf-dist/doc/latex/texshade/AQP1.top
new file mode 100644
index 00000000000..55c62f73681
--- /dev/null
+++ b/Master/texmf-dist/doc/latex/texshade/AQP1.top
@@ -0,0 +1,14 @@
+
+\feature{bottom}{1}{1..14}{'-'}{int.\ A}
+\feature{top}{1}{15..32}{box[LightGray]:TM1}{}
+\feature{top}{1}{33..49}{,-,}{ext.\ B}
+\feature{top}{1}{50..68}{box[LightGray]:TM2}{}
+\feature{bottom}{1}{69..81}{'-'}{int.\ C}
+\feature{top}{1}{82..106}{box[LightGray]:TM3}{}
+\feature{top}{1}{107..136}{,-,}{ext.\ D}
+\feature{top}{1}{137..154}{box[LightGray]:TM4}{}
+\feature{bottom}{1}{155..168}{'-'}{int.\ E}
+\feature{top}{1}{169..186}{box[LightGray]:TM5}{}
+\feature{top}{1}{187..211}{,-,}{ext.\ F}
+\feature{top}{1}{212..230}{box[LightGray]:TM6}{}
+\feature{bottom}{1}{231..269}{'-'}{int.\ G}
diff --git a/Master/texmf-dist/doc/latex/texshade/AQP2spec.ALN b/Master/texmf-dist/doc/latex/texshade/AQP2spec.ALN
new file mode 100644
index 00000000000..2c36822f0f8
--- /dev/null
+++ b/Master/texmf-dist/doc/latex/texshade/AQP2spec.ALN
@@ -0,0 +1,19 @@
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%% %%%%%
+%%%%% This is a minimal .ALN file--many sequence aligners can produce them %%%%%
+%%%%% %%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+AQP2bt SIAFSRAVLAEFLATLLFVFFGLGSALNWPQALPSVLQIAMAFGLAIGTLVQALGHVSGA
+AQP2cf SVAFSRAVFAEFLATLLFVFFGLGSALNWPQALPSVLQIAMAFGLGIGTLVQALGHVSGA
+AQP2dd SIAFSRAVFSEFLATLLFVFFGLGSALNWPQALPSVLQIAMAFGLAIGTLVQALGHISGA
+AQP2ec SIAFSRAVLAEFLATLLFVFFGLGSALNWPQAMPSVLQIAMAFGLAIGTLVQALGHVSGA
+AQP2em SIAFSRAVFSEFLATLLFVFFGLGSALNWPQALPSVLQIAMAFGLAIGTLVQTLGHISGA
+
+AQP2bt HINPAVTVACLVGCHVSFLRAVFYVAAQLLGAVAGAALLHEITPPAIRG
+AQP2cf HINPAVTVACLVGCHVSFLRAAFYVAAQLLGAVAGAALLHEITPPHVRG
+AQP2dd HINPAVTVACLVGCHVSFLRATFYLAAQLLGAVAGAAILHEITPPDIRG
+AQP2ec HINPAVTVACLVGCHVSFLRAAFYVAAQLLGAVAGAALLHEITPPDIRR
+AQP2em HINPAVTVACLVGCHVSFLRATFYLAAQLLGAVAGAALLHELTPPDIRG
+
diff --git a/Master/texmf-dist/doc/latex/texshade/AQPDNA.MSF b/Master/texmf-dist/doc/latex/texshade/AQPDNA.MSF
new file mode 100644
index 00000000000..07707367d8a
--- /dev/null
+++ b/Master/texmf-dist/doc/latex/texshade/AQPDNA.MSF
@@ -0,0 +1,127 @@
+
+AQPDNA.MSF MSF: 979 Type: N Freitag, 12. Februar 1999 Check: 2594 ..
+Name: AQP1nuc.SEQ Len: 807 Check: 8330 Weight: 1.00
+Name: AQP2nuc.SEQ Len: 813 Check: 7220 Weight: 1.00
+Name: AQP3nuc.SEQ Len: 855 Check: 7590 Weight: 1.00
+Name: AQP4nuc.SEQ Len: 960 Check: 8696 Weight: 1.00
+Name: AQP5nuc.SEQ Len: 795 Check: 758 Weight: 1.00
+//
+ 1 60
+AQP1nuc.SEQ ATGGCCAGCGAAATCAAGAAGAAGC.................TCTTCT........GGAG
+AQP2nuc.SEQ ATGTG....GGAACTCAG.........................ATCCAT...........
+AQP3nuc.SEQ ATG........AACC........GTTGCGGGG.AGATG.....CTCC.............
+AQP4nuc.SEQ ATGAGTGACGGAGCTGCAGCGAGGCGGTGGGGTAAGTGTGGACCTCCCTGCAGCAGAGAG
+AQP5nuc.SEQ ATGAAAAA.GGAGGTGTG.........................CTCCCT...........
+
+ 61 120
+AQP1nuc.SEQ GGC..TGTGGTGGCT.....GAGTTCCTGGCCATGA.CCCTCTTCG..............
+AQP2nuc.SEQ ...................................AGCCTTCTCCCGAGCAGTGCTGGCT
+AQP3nuc.SEQ .ACATCC.....GCTACCGG......CTG.........CTTCGCCA....GGCTCTGGCG
+AQP4nuc.SEQ AGCATCATGGTGGCTTTCAAAGGCGTCTGGACTCAAGCCTTCTGGAAGGCGGTCACAGCA
+AQP5nuc.SEQ ...................................TGCCTTCTTCAAGGCGGTGTTCGCA
+
+ 121 180
+AQP1nuc.SEQ ....TCTTCATCAGCATCGGTTCTGCCCTA...GGCTT.....CAATTACCCACTGGAGA
+AQP2nuc.SEQ GAGTTCTTGGCCACGCTCCTTTTTGTCTTCTTTGGCCTTGGCTCAGCCCTCCA.....GT
+AQP3nuc.SEQ GAGTGCCTGGGGACCCTCATCCTTGTGATGTTCGGCTGTGGTTCCGTGGCTCAA.GTGGT
+AQP4nuc.SEQ GAGTTCCTGGCCATGCTCATCTTTGTTCTGCTCAGCGTGGGATCCACCATTAACTGGGGT
+AQP5nuc.SEQ GAGTTCCTGGCCACCCTCATCTTCGTCTTCTTTGGCCTGGGCTCAGCACTCAA.....GT
+
+ 181 240
+AQP1nuc.SEQ GA...AACCAGACGCTGGTCCA.GGACAATGTGAAGGTGTCACTGGCCTTTGGTCTGAGC
+AQP2nuc.SEQ GGGCCAGCT....CCCCACCCTC...TGTGCTCCAGATCGCCGTGGCCTTTGGTCTGGGC
+AQP3nuc.SEQ GCTCAGCCGAGGGACCCATG.GTGG.CTTCCTCACCATCAACTTGGCTTTTGGCTTCGCT
+AQP4nuc.SEQ GGCTCAGAGAACCCCCTACCTGTGGACATGGTCCTCATCTCCCTCTGCTTTGGACTCAGC
+AQP5nuc.SEQ GGCCCTCGG....CTCTGCCCAC...CATTCTGCAAATCTCAATTGCCTTTGGCCTGGCC
+
+ 241 300
+AQP1nuc.SEQ ATCGCTACTCTGGCCCAAAGTGTGGGTCACATCAGTGGTGCTCACTCCAACCCAGCGGTC
+AQP2nuc.SEQ ATCGGCATCCTGGTTCAGGCTCTGGGCCATGTCAGCGGGGCACACATCAACCCCGCCGTG
+AQP3nuc.SEQ GTCACCCTTGCCATCTTGGTGGCTGGCCAAGTGTCTGGAGCCCACTTGAACCCTGCTGTG
+AQP4nuc.SEQ ATTGCCACCATGGTTCAGTGCTTCGGCCACATCAGCGGTGGCCACATCAACCCAGCGGTG
+AQP5nuc.SEQ ATAGGTACCTTAGCCCAAGCTCTGGGACCTGTGAGTGGTGGCCACATCAATCCAGCCATT
+
+ 301 360
+AQP1nuc.SEQ ACACTGGGGCTTCTGCTCAGCTGTCAGATCAGCATCCTCCGGGCTGTCA.TGTATATCAT
+AQP2nuc.SEQ ACTGTGGCATGCCTGGTGGGTTGCCATGTCTCCTTCCTTCGAGCTGCCT.TCTATGTGGC
+AQP3nuc.SEQ ACCTTTGCAATG.TGCTTCCTGGCACGAGAGCCCTGGATCAAGCTGCCCATCTACACACT
+AQP4nuc.SEQ ACAGTGGCCATGGTGTGCACACGAAAGATCAGCATCGCCAAGTCTGTCT.TCTACATCAC
+AQP5nuc.SEQ ACTCTGGCCCTCTTAATAGGAAACCAGATCTCGCTGCTCCGAGCTGTCT.TCTACGTGGC
+
+ 361 420
+AQP1nuc.SEQ CGCCCAGTGTGTGGGAGCCATCGTTGCCTCCGCCATCCTCTCCGGCATCACCTCCTCCCT
+AQP2nuc.SEQ TGCCCAGCTGCTGGGCGCCGTGGCTGGGGCTGCCATCCTCCATGAGATTAC.TCCAGTAG
+AQP3nuc.SEQ GGCACAGACCCTCGGGGCCTTCTTGGGTGCTGGGATTGTTTTTGGGCT..CTACTA..TG
+AQP4nuc.SEQ TGCGCAGTGCCTGGGGGCCATCATCGGAGCTGGGATCCTCTACCTGGTCAC.ACCCCCCA
+AQP5nuc.SEQ AGCCCAGCTGGTGGGCGCCATTGCTGGGGCAGGCATCCTGTACTGGCTGGC.GCCACTCA
+
+ 421 480
+AQP1nuc.SEQ GCTCGAGAACTCACTTGGCCGA.AATGACCTGGCTCGAGGTGTGAACTCCGGCCAGGGCC
+AQP2nuc.SEQ AAATCCGTGGGGACCTGGCTGTCAATGCTCTCCACAACAACGCCACAGCTGGCCAGGCTG
+AQP3nuc.SEQ ATGCAATCTGGGCCTTTGCTGGCAATGAGCT.........TGTTGTCTCCGGCC.....C
+AQP4nuc.SEQ GCGTGGTGGGAGGATTGGGAGTCACCACGGTTCATGGAAACCTCACTGCTGGCCATGGGC
+AQP5nuc.SEQ ATGCCCGGGGTAACCTGGCCGTCAATGCGCTGAACAACAACACAACGCCTGGCAAGGCCA
+
+ 481 540
+AQP1nuc.SEQ TGGGCATTGAGATCATTGGCACCCTGCAGCTGGTGCTGTGCGT.TCTGGCTACCACTGAC
+AQP2nuc.SEQ TGACTGTAGAGCTCTTCCTGACCATGCAGCTGGTGCTGTGCAT.CTTTGCCTCCACCGAC
+AQP3nuc.SEQ CAATGGCACAGCTGGTATC..TTTGCCACCTATCCCTCTGGACACTTGGATATGGTCAAT
+AQP4nuc.SEQ TCCTGGTGGAGCTAATAATCACTTTCCAGCTGGTATTCACCAT.TTTTGCCAGCTGTGAT
+AQP5nuc.SEQ TGGTGGTGGAGTTAATCTTGACTTTCCAGCTAGCCCTCTGCAT.CTTCTCCTCCACCGAC
+
+ 541 600
+AQP1nuc.SEQ CGGAGGCGCCGAGACTTAGGTGGCTCAGCCCCACTTGCCATTGGCTTGTCTGTGGCTCTT
+AQP2nuc.SEQ GAGCGCCGCGGTGACAACCTGGGTAGCCCTGCCCTCTCCATTGGTTTCTCTGTTACCCTG
+AQP3nuc.SEQ GGCTTCTTTGATCAGTTCATAGGCACAGCAGCCCTTATTGTGTGTGTGCTGGCCATTGTT
+AQP4nuc.SEQ TCCAAACGGACTGATGTTACTGGTTCCGTTGCTTTAGCAATTGGGTTTTCCGTTGCAATT
+AQP5nuc.SEQ TCTCGCCGAACCAGCCCTGTGGGCTCCCCAGCCTTATCCATTGGCTTGTCTGTCACACTG
+
+ 601 660
+AQP1nuc.SEQ GGACACCTGCTGGCCATTGACTACACTGGCTGTGGGATCAACCCTGCCCGGTCATT.TGG
+AQP2nuc.SEQ GGCCACCTCCTTGGGATCTATTTCACCGGTTGCTCCATGAATCCAGCCCGCTCCCT.GGC
+AQP3nuc.SEQ GACC..CTTATAACAACCCTGTGCCCCGGGGCCTGGAGGCCTTCACTGTGGGCCTTGTGG
+AQP4nuc.SEQ GGACATTTGTTTGCAATCAATTATACCGGAGCCAGCATGAATCCAGCTCGATCCTT.TGG
+AQP5nuc.SEQ GGCCATCTTGTGGGGATCTACTTCACCGGCTGTTCCATGAACCCAGCCCGATCTTT.CGG
+
+ 661 720
+AQP1nuc.SEQ CTCTGCTGTGCTCACCCGCAACTTCTCAAAC...CACTGGATTTTCTGGGTGGGACCATT
+AQP2nuc.SEQ TCCAGCAGTTGTCACTGGCAAGTTTGATGA...TCACTGGGTCTTCTGGATCGGACCCCT
+AQP3nuc.SEQ TCCTG.....GTCATTGGGACCTCCATGGGCTTCAATTCTGGCTATGCCGTCAACCCAGC
+AQP4nuc.SEQ CCCTGCAGTTATCATGGGAAACTGGGAAAAC...CACTGGATATATTGGGTTGGACCAAT
+AQP5nuc.SEQ CCCTGCGGTGGTCATGAACCGGTTCAGCCCCTCTCACTGGGTCTTCTGGGTAGGGCCTAT
+
+ 721 780
+AQP1nuc.SEQ CATTGGGAGTGCCCTGGCAGTGCTGATCTATGACTTCATC..CTGGCCCCACGC..AGC.
+AQP2nuc.SEQ GGTGGGCGCCATCATCGGCTCCCTCCTCTACAACTAC..CTGCTGTTC..........CC
+AQP3nuc.SEQ T.....CGTGACTTTGG..ACCTCGCCTTTTCACTGCCCTGGCTGGC......TGGGGTT
+AQP4nuc.SEQ CATAGGCGCTGTGCTGGCAGGTGCACTTTACGAGTATGTCTTCTGTCCTGACGTGGAGCT
+AQP5nuc.SEQ TGTGGGGGCCATGCTGGCGGCCATCCTCTATTTCTAC..CTGCTCTTC..........CC
+
+ 781 840
+AQP1nuc.SEQ ..AGCG.........................ACTTTACAG.............ACCGCAT
+AQP2nuc.SEQ C.....TCGGCAAAG...AGCCTGCAGGAGCGCTTGGCAGTGCTCAAGGG.......CCT
+AQP3nuc.SEQ CAGAAGTC.TTTACGACTGGCC...AGAACTGGTGGTGGGTACCCATCGTCTCTCCACTC
+AQP4nuc.SEQ CAAACGTCGCCTAAAGGAAGCCTTCAGCAAAGCTGCACAGCAGACGAAAGGGAGCTACAT
+AQP5nuc.SEQ C.....TCCTCTCTG...AGCCTCCATGATCGCGTGGCTGTCGTCAAAGG.......CAC
+
+ 841 900
+AQP1nuc.SEQ GAAGGTGTGGACCAGT...GGCCAAGTGGA.....GGAGTATGACCTGGATGC.......
+AQP2nuc.SEQ GGAGCCCGACACCGACTGGGA.......GGAACGTGAAGTGCGG..CGGCGGCAGTCGGT
+AQP3nuc.SEQ CTGGGTTC.CATTGGTGGTGTCTTCGTGT.ACCAGCT..CATGAT.TGGCTGCCACC..T
+AQP4nuc.SEQ GGAGGTGGAGGACAACCGGAGCCAAGTGGAGACAGAAGACTTGATCCTGAAGCCCGGGGT
+AQP5nuc.SEQ ATA...TGA.GCCGG..AGGA.......GGACTGGGAAGATCAT..CGAGAGGAGAGGAA
+
+ 901 960
+AQP1nuc.SEQ ........TGAT.GATATCAACTCCAGGGTGGAGATGAAG....................
+AQP2nuc.SEQ GGAGC......TC..CACTCTCCTCAGAG...................CCTGCCTCGCG.
+AQP3nuc.SEQ GGAGCA.GCCCCCGCCTTCCACT..GAGGCAGAGAATGTGAAGCTGG.CCCACATGAAGC
+AQP4nuc.SEQ GGTGCATGTGATCGACATTGACCGTGGAGACGAGAAGAAGGGGAAGGACTCGTCTGGAGA
+AQP5nuc.SEQ GAAG............ACCATC....GAG........................CTGACG.
+
+ 961 979
+AQP1nuc.SEQ ..........CCCAAATAG
+AQP2nuc.SEQ .GCAGCAAGGCCTG....A
+AQP3nuc.SEQ ACAAGGA..GCAGATCTGA
+AQP4nuc.SEQ GGTATTATCTTCTGTATGA
+AQP5nuc.SEQ .GCA.CA....CTG....A
+
diff --git a/Master/texmf-dist/doc/latex/texshade/AQP_HMM.ext b/Master/texmf-dist/doc/latex/texshade/AQP_HMM.ext
new file mode 100644
index 00000000000..d95988ad1e3
--- /dev/null
+++ b/Master/texmf-dist/doc/latex/texshade/AQP_HMM.ext
@@ -0,0 +1,148 @@
+
+Protein: AQP1
+Length: 269
+N-terminus: IN
+Number of transmembrane helices: 6
+Transmembrane helices: 14-33 54-73 94-112 139-156 165-184 211-230
+
+Total entropy of the model: 17.0025
+Entropy of the best path: 17.0049
+
+The best path:
+
+ seq MASEIKKKLF WRAVVAEFLA MTLFVFISIG SALGFNYPLE RNQTLVQDNV 50
+ pred IIIIiiiiii iiiHHHHHHH HHHHHHHHHH HHHooooooo oooooooooo
+
+ seq KVSLAFGLSI ATLAQSVGHI SGAHSNPAVT LGLLLSCQIS ILRAVMYIIA 100
+ pred oooHHHHHHH HHHHHHHHHH HHHiiiiiii iiiiiiiiii iiiHHHHHHH
+
+ seq QCVGAIVASA ILSGITSSLL ENSLGRNDLA RGVNSGQGLG IEIIGTLQLV 150
+ pred HHHHHHHHHH HHoooooooo oooooooooo ooooooooHH HHHHHHHHHH
+
+ seq LCVLATTDRR RRDLGGSAPL AIGLSVALGH LLAIDYTGCG INPARSFGSA 200
+ pred HHHHHHiiii iiiiHHHHHH HHHHHHHHHH HHHHoooooo oooooooooo
+
+ seq VLTRNFSNHW IFWVGPFIGS ALAVLIYDFI LAPRSSDFTD RMKVWTSGQV 250
+ pred oooooooooo HHHHHHHHHH HHHHHHHHHH iiiiiiiiii iiiiiIIIII
+
+ seq EEYDLDADDI NSRVEMKPK 269
+ pred IIIIIIIIII IIIIIIIII
+
+Protein: AQP2
+Length: 271
+N-terminus: IN
+Number of transmembrane helices: 6
+Transmembrane helices: 17-35 44-65 86-104 131-148 157-176 203-224
+
+Total entropy of the model: 17.0017
+Entropy of the best path: 17.0046
+
+The best path:
+
+ seq MWELRSIAFS RAVLAEFLAT LLFVFFGLGS ALQWASSPPS VLQIAVAFGL 50
+ pred IIIIIIiiii iiiiiiHHHH HHHHHHHHHH HHHHHooooo oooHHHHHHH
+
+ seq GIGILVQALG HVSGAHINPA VTVACLVGCH VSFLRAAFYV AAQLLGAVAG 100
+ pred HHHHHHHHHH HHHHHiiiii iiiiiiiiii iiiiiHHHHH HHHHHHHHHH
+
+ seq AAILHEITPV EIRGDLAVNA LHNNATAGQA VTVELFLTMQ LVLCIFASTD 150
+ pred HHHHoooooo oooooooooo oooooooooo HHHHHHHHHH HHHHHHHHii
+
+ seq ERRGDNLGSP ALSIGFSVTL GHLLGIYFTG CSMNPARSLA PAVVTGKFDD 200
+ pred iiiiiiHHHH HHHHHHHHHH HHHHHHoooo oooooooooo oooooooooo
+
+ seq HWVFWIGPLV GAIIGSLLYN YLLFPSAKSL QERLAVLKGL EPDTDWEERE 250
+ pred ooHHHHHHHH HHHHHHHHHH HHHHiiiiii iiiiiiiiiI IIIIIIIIII
+
+ seq VRRRQSVELH SPQSLPRGSK A 271
+ pred IIIIIIIIII IIIIIIIIII I
+
+Protein: AQP3
+Length: 285
+N-terminus: IN
+Number of transmembrane helices: 6
+Transmembrane helices: 22-41 50-72 103-122 153-172 185-207 238-260
+
+Total entropy of the model: 17.0059
+Entropy of the best path: 17.0075
+
+The best path:
+
+ seq MNRCGEMLHI RYRLLRQALA ECLGTLILVM FGCGSVAQVV LSRGTHGGFL 50
+ pred IIIIIIiiii iiiiiiiiii iHHHHHHHHH HHHHHHHHHH HooooooooH
+
+ seq TINLAFGFAV TLAILVAGQV SGAHLNPAVT FAMCFLAREP WIKLPIYTLA 100
+ pred HHHHHHHHHH HHHHHHHHHH HHiiiiiiii iiiiiiiiii iiiiiiiiii
+
+ seq QTLGAFLGAG IVFGLYYDAI WAFAGNELVV SGPNGTAGIF ATYPSGHLDM 150
+ pred iiHHHHHHHH HHHHHHHHHH HHoooooooo oooooooooo oooooooooo
+
+ seq VNGFFDQFIG TAALIVCVLA IVDPYNNPVP RGLEAFTVGL VVLVIGTSMG 200
+ pred ooHHHHHHHH HHHHHHHHHH HHiiiiiiii iiiiHHHHHH HHHHHHHHHH
+
+ seq FNSGYAVNPA RDFGPRLFTA LAGWGSEVFT TGQNWWWVPI VSPLLGSIGG 250
+ pred HHHHHHHooo oooooooooo oooooooooo oooooooHHH HHHHHHHHHH
+
+ seq VFVYQLMIGC HLEQPPPSTE AENVKLAHMK HKEQI 285
+ pred HHHHHHHHHH iiiiiiiiii iiiiiIIIII IIIII
+
+Protein: AQP4
+Length: 323
+N-terminus: IN
+Number of transmembrane helices: 6
+Transmembrane helices: 37-57 70-92 123-147 160-177 186-205 232-254
+
+Total entropy of the model: 17.0058
+Entropy of the best path: 17.0091
+
+The best path:
+
+ seq MSDGAAARRW GKCGPPCSRE SIMVAFKGVW TQAFWKAVTA EFLAMLIFVL 50
+ pred IIIIIIIIII IIIIIIIIII Iiiiiiiiii iiiiiiHHHH HHHHHHHHHH
+
+ seq LSVGSTINWG GSENPLPVDM VLISLCFGLS IATMVQCFGH ISGGHINPAV 100
+ pred HHHHHHHooo oooooooooH HHHHHHHHHH HHHHHHHHHH HHiiiiiiii
+
+ seq TVAMVCTRKI SIAKSVFYIT AQCLGAIIGA GILYLVTPPS VVGGLGVTTV 150
+ pred iiiiiiiiii iiiiiiiiii iiHHHHHHHH HHHHHHHHHH HHHHHHHooo
+
+ seq HGNLTAGHGL LVELIITFQL VFTIFASCDS KRTDVTGSVA LAIGFSVAIG 200
+ pred oooooooooH HHHHHHHHHH HHHHHHHiii iiiiiHHHHH HHHHHHHHHH
+
+ seq HLFAINYTGA SMNPARSFGP AVIMGNWENH WIYWVGPIIG AVLAGALYEY 250
+ pred HHHHHooooo oooooooooo oooooooooo oHHHHHHHHH HHHHHHHHHH
+
+ seq VFCPDVELKR RLKEAFSKAA QQTKGSYMEV EDNRSQVETE DLILKPGVVH 300
+ pred HHHHiiiiii iiiiiiiiiI IIIIIIIIII IIIIIIIIII IIIIIIIIII
+
+ seq VIDIDRGDEK KGKDSSGEVL SSV 323
+ pred IIIIIIIIII IIIIIIIIII III
+
+Protein: AQP5
+Length: 265
+N-terminus: IN
+Number of transmembrane helices: 6
+Transmembrane helices: 13-32 59-78 87-110 131-149 158-177 204-228
+
+Total entropy of the model: 17.0020
+Entropy of the best path: 17.0052
+
+The best path:
+
+ seq MKKEVCSLAF FKAVFAEFLA TLIFVFFGLG SALKWPSALP TILQISIAFG 50
+ pred IIIIIIIIii iiHHHHHHHH HHHHHHHHHH HHoooooooo oooooooooo
+
+ seq LAIGTLAQAL GPVSGGHINP AITLALLIGN QISLLRAVFY VAAQLVGAIA 100
+ pred ooooooooHH HHHHHHHHHH HHHHHHHHii iiiiiiHHHH HHHHHHHHHH
+
+ seq GAGILYWLAP LNARGNLAVN ALNNNTTPGK AMVVELILTF QLALCIFSST 150
+ pred HHHHHHHHHH oooooooooo oooooooooo HHHHHHHHHH HHHHHHHHHi
+
+ seq DSRRTSPVGS PALSIGLSVT LGHLVGIYFT GCSMNPARSF GPAVVMNRFS 200
+ pred iiiiiiiHHH HHHHHHHHHH HHHHHHHooo oooooooooo oooooooooo
+
+ seq PSHWVFWVGP IVGAMLAAIL YFYLLFPSSL SLHDRVAVVK GTYEPEEDWE 250
+ pred oooHHHHHHH HHHHHHHHHH HHHHHHHHii iiiiiiiiii iiiIIIIIII
+
+ seq DHREERKKTI ELTAH 265
+ pred IIIIIIIIII IIIII
diff --git a/Master/texmf-dist/doc/latex/texshade/AQP_HMM.sgl b/Master/texmf-dist/doc/latex/texshade/AQP_HMM.sgl
new file mode 100644
index 00000000000..f3a0a659cd9
--- /dev/null
+++ b/Master/texmf-dist/doc/latex/texshade/AQP_HMM.sgl
@@ -0,0 +1,6 @@
+
+>HP: 269 AQP1 IN 6 14 33 54 73 94 112 139 156 165 184 211 230
+>HP: 271 AQP2 IN 6 17 35 44 65 86 104 131 148 157 176 203 224
+>HP: 285 AQP3 IN 6 22 41 50 72 103 122 153 172 185 207 238 260
+>HP: 323 AQP4 IN 6 37 57 70 92 123 147 160 177 186 205 232 254
+>HP: 265 AQP5 IN 6 13 32 59 78 87 110 131 149 158 177 204 228
diff --git a/Master/texmf-dist/doc/latex/texshade/AQPpro.MSF b/Master/texmf-dist/doc/latex/texshade/AQPpro.MSF
new file mode 100644
index 00000000000..815bb157d66
--- /dev/null
+++ b/Master/texmf-dist/doc/latex/texshade/AQPpro.MSF
@@ -0,0 +1,50 @@
+
+AQPpro.MSF MSF: 356 Type: P Freitag, 12. Februar 1999 Check: 2586 ..
+Name: AQP1.PRO Len: 269 Check: 5367 Weight: 1.00
+Name: AQP2.PRO Len: 271 Check: 6176 Weight: 1.00
+Name: AQP3.PRO Len: 285 Check: 2893 Weight: 1.00
+Name: AQP4.PRO Len: 323 Check: 9737 Weight: 1.00
+Name: AQP5.PRO Len: 265 Check: 8413 Weight: 1.00
+//
+ 1 60
+AQP1.PRO MAS........................EIKKKLFWRAVVAEFLAMTLFVFISIGSALGFN
+AQP2.PRO MW.........................ELRSIAFSRAVLAEFLATLLFVFFGLGSALQWA
+AQP3.PRO M.........NRCG.....EMLHIRYR......LLRQALAECLGTLILVMFGCGSVAQVV
+AQP4.PRO MSDGAAARRWGKCGPPCSRESIMVAFKGVWTQAFWKAVTAEFLAMLIFVLLSVGSTINWG
+AQP5.PRO MK........................KEVCSLAFFKAVFAEFLATLIFVFFGLGSALKWP
+
+ 61 120
+AQP1.PRO YPLERNQTLVQDNVKVSLAFGLSIATLAQSVGHISGAHSNPAVTLGLLLSCQISILRAVM
+AQP2.PRO ...SS....PPSVLQIAVAFGLGIGILVQALGHVSGAHINPAVTVACLVGCHVSFLRAAF
+AQP3.PRO LSRGTHGGF....LTINLAFGFAVTLAILVAGQVSGAHLNPAVTFAMCFLAREPWIKLPI
+AQP4.PRO ...GSENPLPVDMVLISLCFGLSIATMVQCFGHISGGHINPAVTVAMVCTRKISIAKSVF
+AQP5.PRO ...SA....LPTILQISIAFGLAIGTLAQALGPVSGGHINPAITLALLIGNQISLLRAVF
+
+ 121 180
+AQP1.PRO YIIAQCVGAIVASAILSGI..........TSSLLENSLGRNDLARGVNSGQ.....GLGI
+AQP2.PRO YVAAQLLGAVAGAAILHEI..........TPVEIRGDLAVNALHNNATAGQ.....AVTV
+AQP3.PRO YTLAQTLGAFLGAGIVFGLYYDAIWAFAGNELVVSGPNGTAGIFATYPSGHLDMVNGFFD
+AQP4.PRO YITAQCLGAIIGAGILYLV..........TPPSVVGGLGVTTVHGNLTAGH.....GLLV
+AQP5.PRO YVAAQLVGAIAGAGILYWL..........APLNARGNLAVNALNNNTTPGK.....AMVV
+
+ 181 240
+AQP1.PRO EIIGTLQLVLCVLATTDR.RRRDLGGSAPLAIGLSV.ALGHLLAIDYTGCGINPARSFGS
+AQP2.PRO ELFLTMQLVLCIFASTDE.RRGDNLGSPALSIGFSV.TLGHLLGIYFTGCSMNPARSLAP
+AQP3.PRO QFIGTAALIVCVLAIVDPYNNPVPRGLEAFTVGLVVLVIGTSMGFN.SGYAVNPARDFGP
+AQP4.PRO ELIITFQLVFTIFASCDS.KRTDVTGSVALAIGFSV.AIGHLFAINYTGASMNPARSFGP
+AQP5.PRO ELILTFQLALCIFSSTDS.RRTSPVGSPALSIGLSV.TLGHLVGIYFTGCSMNPARSFGP
+
+ 241 300
+AQP1.PRO AVLTR..NFS.N......HWIFWVGPFIGSALAVL..IYDFILAPRSSDFTDRMK.....
+AQP2.PRO AVVTG..KFD.D......HWVFWIGPLVGAIIGSL..LYNYLLFPSAKSLQERL..AVLK
+AQP3.PRO RLFTALAGWGSEVFTTGQNW..WWVPIVSPLLGSIGGVFVYQL.................
+AQP4.PRO AVIMG..NWE.N......HWIYWVGPIIGAVLAGA..LYEYV.FCPDVELKRRLKEAFSK
+AQP5.PRO AVVMN..RFSPS......HWVFWVGPIVGAMLAAI..LYFYLLFPSSLSLHDRV..AVVK
+
+ 301 356
+AQP1.PRO .......VWTS.....GQVEEYDLDAD.......DINSRVEMKPK...........
+AQP2.PRO G.LEPDTDWEEREVRRRQ..SVELHSPQSLPRG...................SKA.
+AQP3.PRO ..................MIGCHLEQPPPSTEAENV.KLAHMKHKE.......QI.
+AQP4.PRO AAQQTKGSYMEVEDNRSQVETEDLILKPGVVHVIDIDRGDEKKGKDSSGEVLSSV.
+AQP5.PRO GTYEPEEDWEDHREERKK..TIELTAH.............................
+
diff --git a/Master/texmf-dist/doc/latex/texshade/README b/Master/texmf-dist/doc/latex/texshade/README
new file mode 100644
index 00000000000..752a63a8538
--- /dev/null
+++ b/Master/texmf-dist/doc/latex/texshade/README
@@ -0,0 +1,149 @@
+ TeXshade v1.12
+ >>
+ >> A LaTeX package for setting nucleotide and peptide alignments.
+ >>
+ >> Setting alignments of nucleotides and peptides for publication
+ >> or presentation purposes is usually a time consuming two-step
+ >> process. First, a scientific software is used for the calcula-
+ >> tion of the alignment. This is done in a few minutes. Then, in
+ >> order to highlight special sequence relationships and to label
+ >> positions and regions of interest a second software with high
+ >> output capability is needed.
+ >>
+ >> Manipulating sequence alignments with standard word processing
+ >> or graphics programs takes its time--often several hours--and
+ >> simple layout changes such as re-breaking lines, say from 50
+ >> to 40 residues per line, elongate the working time considerab-
+ >> ly.
+ >>
+ >> TeXshade is an alignment shading software completely written
+ >> in TeX/LaTeX which can process multiple sequence alignments in
+ >> the MSF, ALN and FASTA file format. It provides in addition to
+ >> common shading algorithms special shading modes featuring
+ >> functional aspects, e.g. charge or hydropathy, and a plenitude
+ >> of commands for handling shading colors, text styles, labels,
+ >> legends and even allows the user to define completely new sha-
+ >> ding modes. TeXshade combines highest flexibility and the
+ >> habitual TeX output quality--with reasonable time expenditure.
+ >>
+ Copyright (C) 1999 - 2005 Eric Beitz
+
+
+
+ FOR THE HASTY READER
+
+ Be sure to use a docstrip version 2.4 or later!
+ Otherwise you will not be able to tex the documentation!
+
+
+
+1 - FILES DISTRIBUTED WITH THIS PACKAGE
+
+ texshade.ins Batch file, run through LaTeX
+ texshade.dtx Docstrip archive, run twice through LaTeX
+ tsfaq.tex Frequently asked questions about TeXshade
+ texshade.txt This file
+
+
+ (a) FILES THAT WILL BE GENERATED FROM TEXSHADE.INS
+
+ texshade.sty LaTeX package
+ texshade.def Standard definitions
+ AQPDNA.MSF Example nucleotide alignment file (MSF-format)
+ AQPpro.MSF Example protein alignment file (MSF-format)
+ AQP2spec.ALN Example protein alignment file (ALN-format)
+ AQP1.top Example topology data file generated from PHD
+ AQP1.phd Example PHD secondary structure file
+ Standard.cod Standard genetic code definitions
+ Ciliate.cod Ciliate macronuclear genetic code definitions
+
+
+ (b) FILE THAT WILL BE GENERATED FROM TEXSHADE.DTX
+
+ texshade.dvi Package documentation
+
+
+
+2 - INSTALLATION
+
+ (a) EXTRACTING FILES FROM THE DOCSTRIP ARCHIVE
+
+ All files provided by TeXshade are compacted to one single file,
+ namely "texshade.dtx". To extract the archive run "texshade.ins"
+ - which contains the corresponding instructions - through LaTeX.
+ A list of the generated files is given above, see 1(a).
+
+ AGAIN: Be sure to use a docstrip version 2.4 or later! Otherwise
+ you will not be able to tex the documentation!
+
+
+ (b) THE DOCUMENTATION
+
+ The file "texshade.dtx" further contains the package documentation.
+ Therefore, run this file through LaTeX now. As you will recognize
+ two runs are needed to make proper references within the document.
+
+ TeXshade needs lots of TeX's memory, so adjust your parameter set-
+ tings to make TeXshade feel comfortable. The documentation is a
+ good test for this. (If you encounter problems texing the doc, you
+ should tex and read section A of the FAQ-list (see d below) or
+ download an on-line version [PDF-, DVI-, or PostScript format] at
+ http://homepages.uni-tuebingen.de/beitz/)
+
+ The resulting file "texshade.dvi" can be viewed and printed using a
+ DVI-viewer which is able to display embedded PostScript. Another
+ possibility is to run "texshade.dvi" through DVIPS, a DVI to Post-
+ Script converter, and finally view and print the converted file
+ which will be most likely "texshade.ps" with GhostView from the GNU
+ free software foundation.
+
+ TeXshade makes use of "color.sty" by David Carlisle. This style is
+ part of the Standard LaTeX Graphics Bundle. Usually, the bundle is
+ present in a comprehensive LaTeX installation. If this is not the
+ case for your system you have to download the package from a CTAN-
+ server, e.g. ftp.dante.de.
+
+
+ (c) MAKING TEXSHADE.STY AVAILABLE FOR YOUR LATEX SYSTEM
+
+ In the final step, copy at least the files "texshade.sty" and
+ "ciliate.cod" to a directory searched by TeX in order to make these
+ files available for all documents you'll produce in the future. The
+ remaining files are example files which are not necessary for run-
+ ning TeXshade. Nevertheless, it would be a good idea to keep all
+ the files together.
+
+
+ (d) THE FAQ LIST
+
+ The FAQ list contains frequently asked questions about the package.
+ Use it as a helpful source for solving problems with TeXshade. You
+ get the list by simply running "tsfaq.tex" through LaTeX once.
+
+
+
+3 - CONTACT
+
+ E-Mail: eric.beitz@uni-tuebingen.de
+ WWW: http://homepages.uni-tuebingen.de/beitz/
+ (On-line documentation and updates)
+ Address: Eric Beitz, Universit"at T"ubingen, Pharmazeutische Chemie,
+ Auf der Morgenstelle 8, D-72076 T"ubingen (Germany)
+
+
+
+4 - AGREEMENT
+
+ This program is free software; you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation; either version 2 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ In order to receive a copy of the GNU General Public License write to
+ the Free Software Foundation, Inc., 59 Temple Place - Suite 330,
+ Boston, MA 02111-1307, USA.
diff --git a/Master/texmf-dist/doc/latex/texshade/ciliate.cod b/Master/texmf-dist/doc/latex/texshade/ciliate.cod
new file mode 100644
index 00000000000..36748d765ed
--- /dev/null
+++ b/Master/texmf-dist/doc/latex/texshade/ciliate.cod
@@ -0,0 +1,19 @@
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%% %%%%%
+%%%%% Ciliate macronuclear genetic code definitions %%%%%
+%%%%% %%%%%
+%%%%% Only exchanges compared to the standard code must be defined. %%%%%
+%%%%% %%%%%
+%%%%% (The last codon of the list is used for backtranslations %%%%%
+%%%%% from protein to DNA sequences---therefore the wobbles) %%%%%
+%%%%% %%%%%
+%%%%% %%%%%
+%%%%% Activate these definitions for your alignment by the following %%%%%
+%%%%% command in the texshade environment: %%%%%
+%%%%% %%%%%
+%%%%% \geneticcode{ciliate} %%%%%
+%%%%% %%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\codon{Q}{TAA,TAG,UAA,UAG,YAR}
diff --git a/Master/texmf-dist/doc/latex/texshade/standard.cod b/Master/texmf-dist/doc/latex/texshade/standard.cod
new file mode 100644
index 00000000000..6eca648651f
--- /dev/null
+++ b/Master/texmf-dist/doc/latex/texshade/standard.cod
@@ -0,0 +1,34 @@
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%% %%%%%
+%%%%% Standard genetic code definitions %%%%%
+%%%%% %%%%%
+%%%%% (The last codon of each list is used for backtranslations %%%%%
+%%%%% from protein to DNA sequences---therefore the wobbles) %%%%%
+%%%%% %%%%%
+%%%%% These definitions are default in TeXshade. %%%%%
+%%%%% There is no need to load them. This is an example file only. %%%%%
+%%%%% %%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\codon{A}{GCA,GCG,GCC,GCT,GCU,GCN}
+\codon{C}{TGC,TGT,UGC,UGU,TGY}
+\codon{D}{GAC,GAT,GAU,GAY}
+\codon{E}{GAA,GAG,GAR}
+\codon{F}{TTC,TTT,UUC,UUU,TTY}
+\codon{G}{GGA,GGG,GGC,GGT,GGU,GGN}
+\codon{H}{CAC,CAT,CAY}
+\codon{I}{ATA,ATC,ATT,AUA,AUC,AUU,ATH}
+\codon{K}{AAA,AAG,AAG,AAR}
+\codon{L}{CTA,CTG,CTC,CTT,TTA,TTG,CUG,CUG,CUC,CUU,UUA,UUG,YTN}
+\codon{M}{ATG,AUG,ATG}
+\codon{N}{AAC,AAT,AAU,AAY}
+\codon{P}{CCA,CCG,CCC,CCT,CCU,CCN}
+\codon{Q}{CAA,CAG,CAR}
+\codon{R}{AGA,AGG,CGA,CGG,CGC,CGT,CGU,MGN}
+\codon{S}{TCT,TCC,TCG,TCA,AGT,AGC,UCU,UCC,UCG,UCA,AGU,WSN}
+\codon{T}{ACT,ACC,ACG,ACA,ACU,ACN}
+\codon{V}{GTA,GTG,GTC,GTT,GUA,GUG,GUC,GUU,GTN}
+\codon{W}{TGG,UGG,TGG}
+\codon{Y}{TAC,TAT,UAC,UAU,TAY}
+\codon{.}{TAA,TAG,TGA,UAA,UAG,UGA,TRR}
diff --git a/Master/texmf-dist/doc/latex/texshade/texshade.pdf b/Master/texmf-dist/doc/latex/texshade/texshade.pdf
new file mode 100644
index 00000000000..d4a4bcd4d81
--- /dev/null
+++ b/Master/texmf-dist/doc/latex/texshade/texshade.pdf
Binary files differ
diff --git a/Master/texmf-dist/doc/latex/texshade/tsfaq.pdf b/Master/texmf-dist/doc/latex/texshade/tsfaq.pdf
new file mode 100644
index 00000000000..53b9b895288
--- /dev/null
+++ b/Master/texmf-dist/doc/latex/texshade/tsfaq.pdf
Binary files differ
diff --git a/Master/texmf-dist/doc/latex/texshade/tsfaq.tex b/Master/texmf-dist/doc/latex/texshade/tsfaq.tex
new file mode 100644
index 00000000000..0ab7a0ec378
--- /dev/null
+++ b/Master/texmf-dist/doc/latex/texshade/tsfaq.tex
@@ -0,0 +1,432 @@
+\documentclass[12pt]{article}
+
+
+\begin{document}
+
+
+\noindent
+Eric Beitz \hfill March 2005
+
+\section*{\TeX{}shade: frequently asked questions }
+\bigskip
+
+This is the sixth update of the FAQ list for \TeX{}shade. Feel free to
+contact me if you have problems, questions or suggestions about the
+package. I will post them and provide hopefully helpful hints in
+future issues of this list.
+\bigskip
+
+\noindent
+\qquad email: \texttt{eric.beitz@uni-tuebingen.de}
+\smallskip
+
+\noindent
+\TeX{}shade:
+\texttt{http://homepages.uni-tuebingen.de/beitz/tse.html}
+
+
+\subsection*{A. Increasing \TeX{}'s memory settings}
+\medskip
+
+ If you are using \TeX{}shade to align several large sequences (about 1000
+ residues/sequence), LaTeX will probably stop compiling and quit with one
+ of the following messages:
+ \texttt{!\ TeX capacity exceeded, sorry [main memory size=384000]} or
+ \texttt{!\ TeX capacity exceeded, sorry [stack size=300]}.
+
+Due to several requests I want to start a list of protocols how
+to increase the standard \TeX{} memory settings for bigger
+alignments. Please contribute to this list by sending me the
+procedure for your particular system.
+
+\begin{enumerate}
+
+ \item
+
+ \textbf{Oz\TeX{} 4.0 for the Macintosh:}
+
+ Find the file `OzTeX:TeX:Configs:Default'. This file contains
+ all memory settings. Look for the section
+ `\% TeX parameters' and increase the values that \TeX{} complains
+ about during the run. You will have to restart Oz\TeX{} before the
+ changes are active.
+
+ For older versions of Oz\TeX{} the configuration file has the
+ same name but the path is somewhat different.
+
+
+ \item
+
+ \textbf{te\TeX{} for *NIX:} (contributed by Joerg Daehn)
+
+ Find the file: `/usr/share/texmf/web2c/texmf.cnf' or
+
+ use \verb|locate texmf.cnf| at the command prompt to find it.
+
+ Login as super user. Backup `texmf.cnf' in case you destroy something and
+ then open the `texmf.cnf' file in your favorite text editor and use its
+ search function to locate \verb|main_memory|. This variable is set to 384000.
+ Change this to some higher value, i.e. 4000000 (works fine for me!). The
+ total amount of memory should not exceed 8000000, so check the other
+ values in that section.
+
+ Next, you want to change the stack size. Search for \verb|stack_size|. This
+ will be set to 300. I changed it to 4000 and it works fine.
+
+ There might be complains by \TeX{} about further specific parameters such
+ as \verb|stack_size|. You find all those in the same file.
+
+ After this you have to run `texconfig init'.
+
+ Logout as root.
+
+ After this all should be set for large alignments. Happy \TeX{}ing!
+
+ The information on how to achieve this was derived from a mail in the
+ te\TeX{} mail archive. The original question was posted by Pascal Francq and
+ answered by Rolf Nieprasch.
+
+
+ \item
+
+ \textbf{MiK\TeX{} for Windows:}
+
+ The MiK\TeX{} documentation describes very detailed how the memory
+ settings can be changed. In brief, you must locate the
+ configuration file `miktex/config/miktex.ini'. In the [MiKTeX]
+ section of this file you find all the parameters you need, e.\,g.\
+ \verb|mem_min|, \verb|mem_max|, \verb|buf_size|, \verb|stack_size| etc.
+
+ It appears, that the standard settings of MiK\TeX{} are bigger
+ than that of other \TeX{} installations, so it may not always be necessary
+ to increase the values.
+
+
+\end{enumerate}
+
+
+\subsection*{B. Problems using \TeX{}shade}
+\medskip
+
+\begin{enumerate}
+
+ \item
+
+ \textbf{I cannot \TeX{} the manual because I get the error
+ message `\texttt{!\ TeX capacity exceeded, sorry \ldots}'.}
+
+ \TeX{}shade needs a lot of memory for setting and shading
+ alignments. The manual is a good test for your memory settings
+ because it uses many alignments and fingerprints, which are
+ in particular memory consuming. If you do not know how to increase
+ \TeX's memory settings, and you do not know a \TeX{} wizard either, then
+ visit the \TeX{}shade homepage at
+ \texttt{http://homepages.uni-tuebingen.de/beitz/tse.html} for
+ downloading the manual in either of three formats: DVI, PDF or
+ PostScript.
+
+
+ \item
+
+ \textbf{I can set my alignment only when I reduce the number of
+ base-pairs by about 11,000. Otherwise I get the `\texttt{!\ TeX
+ capacity exceeded, sorry \ldots}' error.}
+
+ There are several parameters defining \TeX's
+ usable space. If you are a \TeX{} wizard (or you know one)
+ increase the values that
+ \TeX{}shade complains about during the run in order to set
+ bigger alignments. But do not be disappointed when your \TeX{}
+ system will not set an alignment containing thousands of residues.
+ There is definitely an upper limit (probably the new \LaTeX3 will
+ allow you to use even more memory). Setting alignments is a big job for a
+ typesetting system!
+
+
+ \item
+
+ \textbf{I want to align 80 sequences but I get the
+ `\texttt{!\ No room for a new count}' message.}
+
+ For each sequence two counter variables are used by \TeX{}shade,
+ further 14 counters for other purposes are needed (and \TeX{}
+ can handle only 255 counters). This limits the amount of sequences
+ to about 100 in theory. But \LaTeX{} itself and each of
+ the loaded packages allocates more counters further reducing the maximum
+ number of sequences.
+
+
+ \item
+
+ \textbf{I receive error messages `\texttt{!\ Missing \$ inserted}'
+ when \TeX{}ing my alignment. What is wrong?}
+
+ At least one of the sequence names in the alignment file contains an
+ underscore `\_' symbol. This makes \TeX{} to believe you missed to
+ enter math mode because subscript initiated by an underscore is
+ only allowed in math. You need to change the sequence name(s) either in the
+ alignment file using the `find \& replace' option of your editor or
+ by using the \verb|\nameseq| command in the \TeX{}shade environment.
+ Nevertheless, subscript and superscript are permitted in sequence names,
+ e.\,g. \verb|\nameseq{1}{Name$_{sub}^{super}$}| will result in
+ Name$_{sub}^{super}$.
+
+ Since v1.3b \TeX{}shade{} is much more tolerant concering special
+ characters. Get it and read the section about sequence names.
+
+
+ \item
+
+ \textbf{My sequence names start out with a number in the
+ alignment file. Why are they ignored by \TeX{}shade?}
+
+ \TeX{}shade analyzes the first character of each line in the
+ alignment file in order to decide whether it is a comment, a
+ ruler or a sequence line etc. All lines starting out with a
+ non-letter character are interpreted as non-sequence lines. Hence,
+ you have to change those names in the alignment file. If you
+ want to have sequence names starting with a number you can
+ use the \verb|\nameseq| command in the \TeX{}shade environment to
+ introduce the number, e.\,g. \verb|\nameseq{1}{57th sequence}|.
+
+
+ \item
+
+ \textbf{Only a fraction of the residues which are supposed to be
+ shaded actually are. Why?}
+
+ Make sure that \TeX{}shade knows when protein sequences are to be
+ set. Align\-ments in the ALN-format do not contain information about the
+ sequence type (DNA or protein). In such cases DNA sequences are
+ assumed by \TeX{}shade leading to a shading of only A's, C's, G's,
+ R's, T's and Y's. A simple solution is to say \verb|\seqtype{P}| in the
+ \verb|texshade| environment.
+
+
+ \item
+
+ \textbf{Functional shading does not work and I get an error message. Why?}
+
+ Same problem as discussed in the point before this one. Functional
+ shading is permitted only on protein sequences. So, tell \TeX{}shade
+ that you are using a protein alignment.
+
+
+ \item
+
+ \textbf{There is an incompatiblity between \TeX{}shade (v1.2)
+ and the multi-language package `\texttt{babel}'!}
+
+ You are right! The command \verb|\language| is defined in both
+ packages which leads to error messages. This bug is fixed since
+ the release of \TeX{}shade version 1.3 from March 2000. In this
+ version \verb|\language| is replaced by two commands:
+ \verb|\germanlanguage| and \verb|\englishlanguage|.
+
+ \item
+
+ \textbf{\TeX{}shade crashes when dashes ``-'' are used as gap
+ symbols in alignment input files.}
+
+ Yes. Be careful with all kinds of characters that are ``active''
+ in \TeX{}, such as \verb|$ _ ^ & % " \|. The dash is not really active
+ but two or three consecutive dashes are amalgamated to one longer
+ dash in \TeX. Having those characters in an input file might result
+ in unforeseen errors or even crashes.
+
+ \item
+
+ \textbf{I have problems using PHD predictions in \TeX{}shade. An
+ empty \texttt{.top} or \texttt{.sec} file is created.}
+
+ When you do the PHD run do not restrict the calculation to either
+ secondary structure or topology prediction. Turn on everything.
+ Otherwise the output will have some ambiguous lines which can not
+ be interpreted by \TeX{}shade. Result is an empty
+ \texttt{.top} or \texttt{.sec} file.
+
+\end{enumerate}
+
+
+
+\subsection*{C. Changing the output}
+\medskip
+
+\begin{enumerate}
+
+ \item
+
+ \textbf{How can I force \TeX{}shade to print more residues per line?}
+
+ Use the \verb|\residuesperline*| command with the `\verb|*|' extension.
+ This will allow you to set any number of residues per line that is
+ desired, e.\,g. \verb|\residuesperline*{97}|. But then expect numerous
+ `\texttt{!\ Overfull hbox}' errors due to printing lines that
+ are broader than the preset \verb|\textwidth|. The same command
+ without the `\verb|*|' will calculate the highest number of residues
+ fitting in one line and round it to be divisible by five.
+
+
+ \item
+
+ \textbf{Is it possible to add a caption to the \TeX{}shade output?}
+
+ Yes, it is. Since \TeX{}shade v1.5 the \verb|\showcaption|
+ command is
+ available to add captions on the top or the bottom of the
+ alignment. The caption behaves exactly as a figure caption
+ including the style, numbering and appearance in the list of
+ figures.
+ \medskip
+
+ Example: \verb|\showcaption{Nice alignment!}|.
+
+
+ \item
+
+ \textbf{I want a short version of the caption for the `List of
+ Figures'. Is this possible?}
+
+ Yes, with \TeX{}shade v1.9 short captions have been introduced.
+ In addition to \verb|showcaption| use the command
+ \verb|shortcaption{|\emph{text}\verb|}|.
+ \medskip
+
+ Example: \verb|\showcaption{Nice alignment!}|\
+ \verb|\shortcaption{Nice}|.
+
+
+ \item
+
+ \textbf{My alignment file contains the letters `B' and `Z' for
+ Asx and Glx, respectively. How can I apply a special shading for
+ these?}
+
+ Use \verb|\funcgroup| to define `B' and `Z' as functional groups
+ and assign the colors and the printing style, e.\,g.
+ \medskip
+
+ \verb|\funcgroup{B}{White}{Blue}{upper}{up}|
+ \smallskip
+
+ \verb|\funcgroup{Z}{White}{Red}{upper}{up}|
+ \medskip
+
+ or add the new residues to an existing group, e.\,g.
+ \medskip
+
+ \verb|\funcgroup{acidic/amide}{DENQBZ}{Black}{Green}{upper}{up}|.
+
+
+ \item
+
+ \textbf{How can I build a legend using the `\texttt{shadebox}'
+ command?}
+
+ The \verb|\shadebox| command simply prints a color-filled box at
+ the very location it occurs in the text. This means you have to
+ use \verb|\shadebox| in the normal text after the \TeX{}shade environment
+ or inside the caption. You find a minimal example below:
+ \medskip
+
+ \qquad\vbox{%
+ \verb|\begin{texshade}{alignmentfile.MSF}|
+ \medskip
+
+ \qquad \verb|\showcpation{Red box: \shadebox{Red}}|
+ \medskip
+
+ \qquad \emph{further commands, if needed}
+ \medskip
+
+ \verb|\end{texshade}|
+ }
+
+ \medskip
+
+ Legend:
+
+ \qquad\verb|\shadebox{conserved}|: conserved residues
+
+ \qquad\verb|\shadebox{White}|: boring residues
+
+ \qquad\verb|\shadebox{Red}|: exciting residues
+
+
+
+ \item
+
+ \textbf{I do not like the spacing between the feature lines. How
+ can I change it?}
+
+ Employ the respective space controlling command from the
+ following list \verb|\ttopspace|,
+ \verb|\topspace|, \verb|\bottomspace|, \verb|\bbottomspace|.
+ Those are available since \TeX{}shade v1.5 (see manual).
+
+
+
+ \item
+
+ \textbf{How can I change gap and match symbols in diverse mode?}
+
+ Since \TeX{}shade version 1.7, standard definitions for \verb|diverse|
+ mode are:
+
+ \begin{verbatim}
+ \nomatchresidues{Black}{White}{lower}{up}
+ \similarresidues{Black}{White}{lower}{up}
+ \conservedresidues{Black}{White}{{.}}{up}
+ \allmatchresidues{Black}{White}{{.}}{up}
+ \gapchar{-}
+ \end{verbatim}
+
+ After calling \verb|\shadingmode{diverse}| these commands can be
+ used to redefine the \verb|diverse| mode settings (mind the double
+ curly braces around the dot-symbol!).
+
+
+
+ \item
+
+ \textbf{I want to rotate the alignment on the page. Is this possible?}
+
+ Yes. Stefan Vogt has found this solution: use pdflscape.sty and
+ activate it in the preamble with \verb|\usepackage{pdflscape}|. Then
+ put your \TeX{}shade environment inside a \verb|landscape|-environment.
+ You also need to adjust the number of residues per line with
+ \verb|\residuesperline*{number}| to make them fill the rotated page.
+ \medskip
+
+ \qquad\vbox{%
+ \verb|\begin{landscape}|
+
+ \verb|\centering|
+
+ \qquad \verb|\begin{texshade}{alignmentfile.MSF}|
+
+ \qquad \verb|\residuesperline*{|\emph{number}\verb|}|
+ \medskip
+
+ \qquad \qquad \emph{further commands, if needed}
+ \medskip
+
+ \qquad \verb|\end{texshade}|
+
+ \verb|\end{landscape}|
+ }
+
+
+
+ \item
+
+ \textbf{I want use the \TeX{}shade and \TeX{}topo logos in my text. How?}
+
+ Use the commands: \verb|\TeXshade| and \verb|\TeXtopo|.
+
+
+\end{enumerate}
+
+
+
+\end{document} \ No newline at end of file